1. R. Rangarajan, R. Raich, and A. O. Hero III, “Sparsity
constrained multidimensional scaling for blind tracking in sensor
networks,” in Advances in Sensor Networks, V. Saligrama, Ed. NY:
Springer, 2007.
Journal papers
39. Trung Vu and Raviv Raich, “On Local Linear Convergence
of Projected Gradient Descent for Constrained Least Squares”, IEEE
Transactions on Signal Processing, vol. 70, pp. 4061-4076, 2022.
38. Trung Vu and Raviv Raich, “A Closed-Form Bound on the Asymptotic
Linear Convergence of Iterative Methods via Fixed Point Analysis”,
Optimization Letters, Springer, May. 2022, accepted.
37. Sharmin Kibria, Jinsub Kim, and Raviv Raich, “Joint nonlinear
sparse error correction for robust state estimation,” IEEE Trans.
Signal Processing, vol. 69, pp. 5859–5874, 2021.
36. Trung Vu, Evgenia Chunikhina, and Raviv Raich, “Perturbation
expansions and error bounds for the truncated singular value
decomposition,” Linear Algebra and its Applications, vol. 627, pp.
94–139, 2021.
35. Tam Nguyen and Raviv Raich, "Incomplete Label Multiple Instance
Multiple Label Learning," in IEEE Transactions on Pattern Analysis
and Machine Intelligence, vol. 44, no. 3, pp. 1320-1337, 1 March
2022.
34. Trung Vu, Phung Lai, Raviv Raich, Ahn Pham, Xiaoli Z. Fern, and
U. A. Rao, “A novel attribute-based symmetric multiple instance
learning for histopathological image analysis,” IEEE Transactions on
Medical Imaging, vol. 39, no. 10, pp. 3125–3136, 2020.
33. Shai Kendler, Ran Aharoni, Shay Cohen, Raviv Raich, Shay Weiss,
Haim Levy, Ziv Mano, Barak Fishbain, and Izhar Ron, “Non-contact and
non-destructive detection and identification of bacillus anthracis
inside paper envelopes,” Forensic science international, vol. 301,
pp. e55 – e58, 2019.
32. Shai Kendler, Izhar Ron, Shay Cohen, Raviv Raich, Ziv Mano, and
Barak Fishbain, “Detection and identification of sub-millimeter
films of organic compounds on environmental surfaces using
short-wave infrared hyperspectral imaging: Algorithm development
using a synthetic set of targets,” IEEE Sensors Journal, vol. 19,
no. 7, pp. 2657–2664, 2018.
31. Zeyu You, Raviv Raich, Xiaoli Z Fern, and Jinsub Kim, “Weakly
supervised dictionary learning,” IEEE Trans. Signal Processing, vol.
66, no. 10, pp. 2527–2541, 2018.
30. Anh T. Pham, Raviv Raich, and Xiaoli Z. Fern, “Dynamic
programming for instance annotation in multi-instance multi-label
learning,” IEEE Trans. on Pattern Analysis and Machine Intelligence,
vol. 39, no. 12, pp. 2381–2394, Dec 2017.
29. Ziwei Ke, Julia Zhang, and Raviv Raich, “Low-frequency current
oscillation reduction for six-step operation of three-phase
inverters,” IEEE Transactions on Power Electronics, vol. 32, no. 4,
pp. 2948–2956, April 2017.
28. Jose F. Ruiz-Mu˜noz, Zeyu You, Raviv Raich, and Xiaoli Z. Fern,
“Dictionary learning for bioacoustics monitoring with applications
to species classification,” Journal of Signal Processing Systems,
pp. 1–15, 2016.
27. Forrest Briggs, Xiaoli Z Fern, and Raviv Raich, “Context-aware
miml instance annotation: exploiting label correlations with
classifier chains,” Knowledge and Information Systems, vol. 43, no.
1, pp. 53–79, 2015.
26. Mohamed R Amer, Siavash Yousefi, Raviv Raich, and Sinisa
Todorovic, “Monocular extraction of 2.1 d sketch using constrained
convex optimization,” International Journal of Computer Vision, vol.
112, no. 1, pp. 23–42, 2015.
25. Gaole Jin and Raviv Raich, “Hinge loss bound approach for
surrogate supervision multi-view learning,” Pattern Recognition
Letters, vol. 37, pp. 143–150, 2014.
24. Forrest Briggs, Xiaoli Z Fern, Raviv Raich, and Qi Lou,
“Instance annotation for multi-instance multilabel learning,” ACM
Transactions on Knowledge Discovery from Data (TKDD), vol. 7, no. 3,
pp. 14, 2013.
23. B. Behmardi and R. Raich, “On confidence-constrained rank
recovery in topic models,” IEEE Trans. Signal Processing, vol. 60,
no. 10, pp. 5146–5162, 2012. (.pdf)
22. K. Sricharan, R. Raich, and A. O. Hero, “Estimation of nonlinear
functionals of densities with confidence,” IEEE Trans. on Inform.
Theory, vol. 58, no. 7, pp. 4135–4159, 2012. (.pdf)
21. C.S. Withers, S. Nadarajah, O. Nørkli, R. Raich, and R.G.
Vaughan, “Estimating complex covariance by observing two variables
at a time,” Acta Mathematica Sinica, English Series, pp. 1–14, 2012.
20. Forrest Briggs, Balaji Lakshminarayanan, Lawrence Neal, Xiaoli
Fern, Raviv Raich, Matthew G. Betts, Sarah Frey, Adam Hadley, and
Matthew G. Betts, “Acoustic classification of multiple simultaneous
bird species: a multi-instance multi-label approach,” Journal of the
Acoustical Society of America, vol. 131, no. 6, pp. 4640–4650, 2012.
19. W.G. Finn, A.M. Harrington, K.M. Carter, R. Raich, S.H. Kroft,
and A.O. Hero III, “Immunophenotypic signatures of benign and
dysplastic granulopoiesis by cytomic profiling,” Cytometry Part B:
Clinical Cytometry, 2011.
18. K. M. Carter, R. Raich, W. G. Finn, and A. O. Hero,
“Information-geometric dimensionality reduction,” Signal Processing
Magazine, IEEE, vol. 28, no. 2, pp. 89–99, 2011.
17. K. M. Carter, R. Raich, and A. O. Hero, “On local intrinsic
dimension estimation and its applications,” IEEE Trans. Signal
Processing, vol. 58, no. 2, pp. 650–663, Feb. 2010.
16. K. M. Carter, R. Raich, W. G. Finn, and A. O. Hero, “FINE:
Fisher information non-parametric embedding,” IEEE Trans. on Pattern
Analysis and Machine Intelligence, vol. 31, no. 11, pp. 2093–2098,
Nov. 2009.
15. M. Ting, R. Raich, and A. O. Hero III, “Sparse image
reconstruction for molecular imaging,” IEEE Transactions on Image
Processing, vol. 18, no. 6, pp. 1215–1227, June 2009.
14. K. Carter, R. Raich, W.G. Finn, and A. O. Hero, “Information
preserving component analysis: data projections for flow cytometry
analysis,” IEEE J. Sel. Topics in Signal Process. (JSTSP), vol. 3,
no. 1, pp. 148–158, Feb. 2009.
13. W. G. Finn, K. M. Carter, R. Raich, L. M. Stoolman, and A. O.
Hero, “Analysis of clinical flow cytometric immunophenotyping data
by clustering on statistical manifolds: Treating flow cytometry data
as highdimensional objects,” Cytometry: Part B - Clinical Cytometry,
vol. 76B, pp. 1–7, Jan. 2009, Best Original Paper published in
Clinical Cytometry for 2008-2009.
12. E. Bashan, R. Raich, and A. O. Hero III, “Optimal two-stage
search for sparse targets using convex criteria,” IEEE Trans. Signal
Processing, vol. 56, no. 11, pp. 5389–5402, Nov. 2008.
11. R. Rangarajan, R. Raich, and A. O. Hero III, “Optimal sequential
energy allocation for inverse problems,” IEEE J. Sel. Topics in
Signal Process. (JSTSP), vol. 1, no. 1, pp. 67–78, June 2007.
10. Kerkil Choi, Aaron D. Lanterman, and Raviv Raich, “Convergence
of the Schulz-Snyder phase retrieval algorithm to local minima,”
Journal of the Optical Society of America A, vol. 23, no. 8, pp.
1835–1845, Aug. 2006.
9. R. Raich, H. Qian, and G. T. Zhou, “Optimization of SNDR for
amplitude-limited nonlinearities,” IEEE Trans. on Communications,
vol. 53, no. 11, pp. 1964–1972, Nov. 2005.
8. Raviv Raich, G. Tong Zhou, and Mats Viberg, “Subspace based
approaches for Wiener system identification,” IEEE Transactions on
Automatic Control, vol. 50, no. 10, pp. 1629–1634, Oct. 2005.
7. G. T. Zhou, H. Qian, L. Ding, and R. Raich, “On the baseband
representation of a bandpass nonlinearity,” IEEE Trans. Signal
Processing, vol. 53, no. 8, pp. 2953–2957, Aug. 2005.
6. R. Raich and G. T. Zhou, “Orthogonal polynomials for complex
Gaussian processes,” IEEE Trans. Signal Processing, vol. 52, no. 10,
pp. 2788–2797, Oct. 2004.
5. R. Raich, H. Qian, and G. T. Zhou, “Orthogonal polynomials for
power amplifier modeling and predistorter design,” IEEE Trans. on
Vehicular Technology, vol. 53, no. 5, pp. 1468–1479, Sept. 2004.
4. G. T. Zhou and R. Raich, “Spectral analysis of polynomial
nonlinearity with applications to RF power amplifiers,” EURASIP
Journal on Applied Signal Processing, Special Issue on Nonlinear
Signal and Image Processing, vol. 2004, pp. 1831–1840, Sept. 2004.
3. J. Friedmann, R. Raich, J. Goldberg, and H. Messer, “Bearing
estimation for a distributed source of non constant modulus,” IEEE
Trans. Signal Processing, vol. 51, no. 12, pp. 3027–3035, Dec. 2003.
2. R. Raich, J. Goldberg, and H. Messer, “Bearing estimation for a
distributed source: Modeling, inherent accuracy limitations and
algorithms,” IEEE Trans. Signal Processing, vol. 48, no. 2, pp.
429–441, Feb. 2000.
1. R. Raich and R.G. Vaughan, “Source power distribution for a
multipath environment using a near field circular array beamformer,”
IEE Electronics Letters, vol. 35, no. 22, pp. 1893–1894, Oct. 1999.
Conference papers
105. Jarrod Hollis, Jinsub Kim, and Raviv Raich,
“Adversarial learning via probabilistic proximity analysis,” in
Proc. IEEE Intl. Conf. Acoust., Speech, Signal Processing, 2021, pp.
3830–3834.
104. Trung Vu and Raviv Raich, “Exact linear convergence rate
analysis for low-rank matrix completion via gradient descent,” in
Proc. IEEE Intl. Conf. Acoust., Speech, Signal Processing, 2021, pp.
3240–3244.
103. J. Hollis, R. Raich, J. Kim, B. Fishbain, and S. Kendler,
“Foreground signature extraction for an intimate mixing model in
hyperspectral image classification,” in Proc. IEEE Intl. Conf.
Acoust., Speech, Signal Processing, 2020, pp. 4732–4736.
102. S. De Silva, J. Kim, and R. Raich, “Cost aware adversarial
learning,” in Proc. IEEE Intl. Conf. Acoust., Speech, Signal
Processing, 2020, pp. 3587–3591.
101. Trung Vu, Raviv Raich, and Xiao Fu, “On convergence of
projected gradient descent for minimizing a large-scale quadratic
over the unit sphere,” in Proc. IEEE International Workshop on
Machine Learning for Signal Processing. IEEE, 2019, pp. 1–6, (best
student paper award).
100. Trung Vu and Raviv Raich, “Accelerating iterative hard
thresholding for low-rank matrix completion via adaptive restart,”
in Proc. IEEE Intl. Conf. Acoust., Speech, Signal Processing. IEEE,
2019, pp. 2917–2921.
99. Trung Vu and Raviv Raich, “Local convergence of the heavy ball
method in iterative hard thresholding for low-rank matrix
completion,” in Proc. IEEE Intl. Conf. Acoust., Speech, Signal
Processing. IEEE, 2019, pp. 3417–3421.
98. Yuanli Pei, Xiaoli Z. Fern, and Raviv Raich, “Learning with
latent label hierarchy from incomplete multi-label data,” in
International Conference on Pattern Recognition (ICPR), Beijing,
China, Aug. 20- 24 2018.
97. Zeyu You, Raviv Raich, Xiaoli Z. Fern, and Jinsub Kim, “Weakly
supervised learning of multiplescale dictionaries,” in Proc. of IEEE
Workshop on Statistical Signal Processing, Freiburg, Germany, June
10-13 2018, Accepted.
96. Trung Vu and Raviv Raich, “Adaptive step size momentum method
for deconvolution,” in Proc. of IEEE Workshop on Statistical Signal
Processing, Freiburg, Germany, June 10-13 2018, Accepted.
95. Anh T. Pham and Raviv Raich, “Differential privacy for positive
and unlabeled learning with known class priors,” in Proc. of IEEE
Workshop on Statistical Signal Processing, Freiburg, Germany, June
10-13 2018, Accepted.
94. Phung Lai, Raviv Raich, and Molly Megraw, “Convmd: Convolutive
matrix decomposition for classification of matrix data,” in Proc. of
IEEE Workshop on Statistical Signal Processing, Freiburg, Germany,
June 10-13 2018, Accepted.
93. Anh T. Pham, Raviv Raich, and Xiaoli Z. Fern, “Discriminative
clustering with cardinality constraints,” in Proc. IEEE Intl. Conf.
Acoust., Speech, Signal Processing, Calgary, Canada, April 15-20
2018, Accepted (best student paper award).
92. Anh T. Pham, Raviv Raich, Xiaoli Z. Fern, Weng-Keen Wong, and
Xinze Guan, “Discriminative probabilistic framework for generalized
multi-instance learning,” in Proc. IEEE Intl. Conf. Acoust., Speech,
Signal Processing, Calgary, Canada, April 15-20 2018, Accepted.
91. Tam Nguyen, Raviv Raich, Xiaoli Z. Fern, and Anh T. Pham,
“MIML-AI: Mixed-supervision multiinstance multi-label learning with
auxiliary information,” in Proc. IEEE International Workshop on
Machine Learning for Signal Processing. IEEE, 2017, pp. 1–6,
Accepted.
90. Shashini De Silva, Jinsub Kim, and Raviv Raich, “Unsupervised
multiview learning with partial distribution information,” in Proc.
IEEE International Workshop on Machine Learning for Signal
Processing. IEEE, 2017, pp. 1–6, Accepted.
89. Xingyi Li, Fuxin Li, Xiaoli Fern, and Raviv Raich, “Filter
shaping for convolutional neural networks,” in International
Conference on Learning Representations, Toulon, France, April 24-26
2017.
88. Revathy Narasimhan, Xiaoli Z. Fern, and Raviv Raich,
“Simultaneous segmentation and classification of bird song using
cnn,” in Proc. IEEE Intl. Conf. Acoust., Speech, Signal Processing,
New-Orleans, LA, USA, March 5-9 2017, pp. 146–150.
87. Zeyu You, Raviv Raich, and Jinsub Kim Xiaoli Z. Fern,
“Discriminative recurring signal detection and localization,” in
Proc. IEEE Intl. Conf. Acoust., Speech, Signal Processing,
New-Orleans, LA, USA, March 5-9 2017, pp. 2377–2381.
86. Jose F. Ruiz-Mu˜noz, Raviv Raich, Mauricio Orozco-Alzate, and
Xiaoli Z. Fern, “Online learning of time-frequency patterns,” in
Proc. IEEE Intl. Conf. Acoust., Speech, Signal Processing,
New-Orleans, LA, USA, March 5-9 2017, pp. 2811–2815.
85. Sharmin Kibria, Jinsub Kim, and Raviv Raich, “Sparse error
correction with multiple measurement vectors: Observability-aware
approach,” in Proc. IEEE Intl. Conf. Acoust., Speech, Signal
Processing, New-Orleans, LA, USA, March 5-9 2017, pp. 4351–4355.
84. Sharmin Kibria, Jinsub Kim, and Raviv Raich, “Sparse error
correction with multiple measurement vectors,” in Statistical Signal
Processing Workshop (SSP), 2016 IEEE. IEEE, 2016, pp. 1–5.
83. Tam Nguyen, Raviv Raich, and Phung Lai, “Jeffreys prior
regularization for logistic regression,” in Statistical Signal
Processing Workshop (SSP), 2016 IEEE. IEEE, 2016, pp. 1–5.
82. Zeyu You, Raviv Raich, Xiaoli Z Fern, and Jinsub Kim,
“Weakly-supervised analysis dictionary learning with cardinality
constraints,” in Statistical Signal Processing Workshop (SSP), 2016
IEEE. IEEE, 2016, pp. 1–5.
81. Raviv Raich and Jinsub Kim, “On the eigenvalue distribution of
column sub-sampled semi-unitary matrices,” in Statistical Signal
Processing Workshop (SSP), 2016 IEEE. IEEE, 2016, pp. 1–5.
80. Xinze Guan, Raviv Raich, and Weng-keen, “Efficient
multi-instance learning for activity recognition from time series
data using an auto-regressive hidden markov model,” in Proceedings
of the 32nd International Conference on Machine Learning, New-York
City, New-York, June. 19-24 2016.
79. Forrest Briggs, Xiaoli Z Fern, Raviv Raich, and Matthew Betts,
“Multi-instance multi-label class discovery: A computational
approach for assessing bird biodiversity,” in Thirtieth AAAI
Conference on Artificial Intelligence, 2016.
78. Xinze Guan, Raviv Raich, and Weng-Keen Wong, “Multi-instance
learning for activity recognition from time series data using a
mixture of auto-regressive processes,” in NIPS time series workshop,
Montreal, Canada, Dec. 7-24 2015.
77. Anh Pham, Raviv Raich, Xiaoli Fern, and Jes´us P Arriaga,
“Multi-instance multi-label learning in the presence of novel class
instances,” in Proceedings of the 32nd International Conference on
Machine Learning, 2015, pp. 2427–2435.
76. Anh T Pham, Raviv Raich, and Xiaoli Z Fern, “Simultaneous
instance annotation and clustering in multi-instance multi-label
learning,” in Machine Learning for Signal Processing (MLSP), 2015
IEEE 25th International Workshop on. IEEE, 2015, pp. 1–6, Best
paper award.
75. JF Ruiz-Mu˜noz, Zeyu You, Raviv Raich, and Xiaoli Z Fern,
“Dictionary extraction from a collection of spectrograms for
bioacoustics monitoring,” in 2015 IEEE 25th International Workshop
on Machine Learning for Signal Processing (MLSP). IEEE, 2015, pp.
1–6.
74. Teresa V Tjahja, Xiaoli Z Fern, Raviv Raich, and Anh T Pham,
“Supervised hierarchical segmentation for bird song recording,” in
Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE
International Conference on. IEEE, 2015, pp. 763–767.
73. Xin Li and Raviv Raich, “Performance analysis of surrogate
supervision multi-view learning linear classifiers in gaussian
data,” in Proc. IEEE International Workshop on Machine Learning for
Signal Processing, Reims, France, Sept. 21-24 2014, IEEE, pp. 1–6.
72. Anh T. Pham, Raviv Raich, and Xiaoli Z. Fern, “Efficient
instance annotation in multi-instance learning,” in Proc. of IEEE
Workshop on Statistical Signal Processing, Gold Coast, Australia,
June 29-July 2 2014, IEEE, pp. 137–140.
71. Anh T. Pham and Raviv Raich, “Kernel-based instance annotation
in multi-instance multi-label learning,” in Proc. IEEE International
Workshop on Machine Learning for Signal Processing, Reims, France,
Sept. 21-24 2014, IEEE, pp. 1–6.
70. Evgenia Chunikhina, Raviv Raich, and Thinh Nguyen, “Performance
analysis for matrix completion via iterative hard-thresholded svd,”
in Proc. of IEEE Workshop on Statistical Signal Processing, Gold
Coast, Australia, June 29-July 2 2014, IEEE, pp. 392–395.
69. Raviv Raich and Zeyu You, “Looking for the same needle in
multiple haystacks: Performance bounds,” in Proc. IEEE Intl. Conf.
Acoust., Speech, Signal Processing, Florence, Italy, May 4-9 2014,
IEEE, pp. 4533–4537.
68. Zeyu You, Raviv Raich, and Yonghong Huang, “An inference
framework for detection of home appliance activation from voltage
measurements,” in Proc. IEEE Intl. Conf. Acoust., Speech, Signal
Processing, Florence, Italy, May 4-9 2014, IEEE, pp. 6033–6037.
67. Zeyu You, Raviv Raich, and Yonghong Huang, “Mixture modeling and
inference for recognition of multiple recurring unknown patterns,”
in Neural Networks (IJCNN), 2014 International Joint Conference on,
Beijing, China, July 6-11 2014, IEEE, pp. 2556–2563.
66. Forrest Briggs, Xiaoli Z. Fern, and Raviv Raich, “Context-aware
MIML instance annotation,” in Proc. IEEE International Conference on
Data Mining, Dallas, Texas, Dec. 7-10 2013, pp. 41–50.
65. Raviv Raich, “A theoretical framework for surrogate supervision
multiview learning,” in Proc. IEEE Global Conference on Signal and
Information Processing, Austin, Texas, Dec. 3-5 2013, pp. 1005–1008.
64. Gaole Jin, Raviv Raich, and David J. Miller, “A generative
semi-supervised model for multi-view learning when some views are
label-free,” in Proc. IEEE Intl. Conf. Acoust., Speech, Signal
Processing, Vancouver, Canada, 2013, pp. 3302–3306.
63. Qi Lou, Raviv Raich, Forrest Briggs, and Xiaoli Z. Fern,
“Novelty detection under multi-label multi-instance framework,” in
Proc. IEEE International Workshop on Machine Learning for Signal
Processing, Southampton, UK, Sept. 22-25 2013, pp. 1–6.
62. Forrest Briggs, Xiaoli Z. Fern, and Raviv Raich, “Rank-loss
support instance machines for MIML instance annotation,” in Proc. of
the 18th ACM SIGKDD Conf. on Knowledge Discovery and Data Mining,
Beijing, China, Aug. 12-16 2012, pp. 534–542.
61. Gaole Jin and Raviv Raich, “On surrogate supervision multiview
learning,” in Proc. IEEE International Workshop on Machine Learning
for Signal Processing, Santander, Spain, Sept. 23-26 2012, pp. 1–6.
60. Behrouz Behmardi, Forrest Briggs, Xiaoli Z. Fern, and Raviv
Raich, “Regularized joint density estimation for multi-instance
learning,” in Proc. of IEEE Workshop on Statistical Signal
Processing, Ann-Arbor, MI, 2012, pp. 740–743.
59. Evgenia Chunikhina, Gregory Gutshall, Raviv Raich, and Thinh
Nguyen, “Tuning-free joint sparse recovery via optimization
transfer,” in Proc. IEEE Intl. Conf. Acoust., Speech, Signal
Processing, Kyoto, Japan, 2012, pp. 1913–1916.
58. Tommy S. Alstrøm, Raviv Raich, Natalie V. Kostesha, and Jan
Larsen, “Feature extraction using distribution representation for
colorimetric sensor arrays used as explosives detectors,” in Proc.
IEEE Intl. Conf. Acoust., Speech, Signal Processing, Kyoto, Japan,
2012, pp. 2125–2128.
57. C. Jinzhou, R. Raich, G. Cauwenberghs, and G. Temes,
“Multi-channel mixed-signal noise source with applications to
stochastic equalization,” in proc. IEEE International Symposium on
Circuits and Systems, Seoul, Korea, 2012, pp. 2497–2500.
56. B. Behmardi and R. Raich, “Convex optimization for exact rank
recovery in topic models,” in Proc. IEEE International Workshop on
Machine Learning for Signal Processing. IEEE, 2011, pp. 1–6.
55. B. Lakshminarayanan and R. Raich, “Inference in supervised
latent dirichlet allocation,” in Proc. IEEE International Workshop
on Machine Learning for Signal Processing, Beijing, China, 2011,
IEEE, pp. 1–6.
54. K. Sricharan, R. Raich, and A. O. Hero, “k-nearest neighbor
estimation of entropies with confidence,” in proc. IEEE
International Symposium on Information Theory, Saint Petersburg,
Russia, 2011, IEEE, p. 1205-1209.
53. B. Behmardi and R. Raich, “On provable exact low-rank recovery
in topic models,” in Proc. of IEEE Workshop on Statistical Signal
Processing, Nice, France, 2011, IEEE, pp. 265–268.
52. B. Behmardi, R. Raich, and A.O. Hero, “Entropy estimation using
the principle of maximum entropy,” in Proc. IEEE Intl. Conf.
Acoust., Speech, Signal Processing, Prague, Czech Republic, 2011,
IEEE, pp. 2008–2011.
51. L. Neal, F. Briggs, R. Raich, and X.Z. Fern, “Time-frequency
segmentation of bird song in noisy acoustic environments,” in Proc.
IEEE Intl. Conf. Acoust., Speech, Signal Processing, Prague, Czech
Republic, 2011, IEEE, pp. 2012–2015.
50. B. Behmardi and R. Raich, “Isometric correction for manifold
learning,” in AAAI 2010 Fall Symposium on Manifold Learning and its
Applications, Arlington, VA, 2010.
49. Mohamed Amer, Raviv Raich, and Sinisa Todorovic, “Monocular
extraction of the 2.1D sketch,” in Proc. IEEE Int. Conf. Image
Processing, Hong Kong, 2010, accepted.
48. B. Lakshminarayanan and R. Raich, “A non-negative matrix
factorization for hidden markov models,” in Proc. IEEE International
Workshop on Machine Learning for Signal Processing, Kittil¨a,
Finland, 2010, pp. 89–94.
47. K. Sricharan, R. Raich, and Alfred O. Hero III, “Boundary
compensated k-nn graphs,” in Proc. IEEE International Workshop on
Machine Learning for Signal Processing, Kittil¨a, Finland, 2010, pp.
277–282.
46. Kumar Sricharan, Raviv Raich, and Alfred O. Hero III, “Optimized
intrinsic dimension estimator using nearest neighbor graphs,” in
Proc. IEEE Intl. Conf. Acoust., Speech, Signal Processing, Dallas,
Texas, 2010, pp. 5418–5421.
45. B. Lakshminarayanan, R. Raich, and X. Fern, “A syllable-level
probabilistic framework for bird species identification,” in Proc.
IEEE International Conference on Machine Learning and Applications,
Miami Beach, Florida, 2009, pp. 53–59, accepted as regular paper.
44. F. Briggs, R. Raich, and X. Fern, “Audio Classification of Bird
Species: A Statistical Manifold Approach,” in Proc. IEEE
International Conference on Data Mining, Miami, Florida, 2009, pp.
51–60, accepted as regular paper (9% acceptance rate).
43. T. Tran, T. Nguyen, and R. Raich, “On achievable throughput
region of prioritized transmission,” in proc. 18th IEEE Conference
on Computer Communications and Networks, San Francisco, CA, 2009,
pp. 1–6, (acceptance rate 29%).
42. M. Thangavelu and R. Raich, “On linear dimension reduction for
multiclass classification of Gaussian mixtures,” in Proc. IEEE
International Workshop on Machine Learning for Signal Processing,
Grenoble, France, 2009, pp. 1–6.
41. K. Carter, R. Raich, and A. O. Hero, “Spherical laplacian
information maps (slim) for dimensionality reduction,” in proc. of
IEEE Workshop on Statistical Signal Processing, Cardiff, UK, 2009,
pp. 405–408.
40. K. Sricharan, R. Raich, and A. O. Hero, “Global performance
prediction for divergence-based image registration,” in proc. of
IEEE Workshop on Statistical Signal Processing, Cardiff, UK, 2009,
pp. 654–657.
39. W. G. Finn, K. M. Carter, R. Raich, A. Harrington, S. H. Kroft,
and A. O. Hero, “Flow cytometric evaluation of reactive and
dysplastic granulocyte maturation by a novel method of high
dimensional data analysis,” in proc. of the United States and
Canadian Academy of Pathology, Boston, MA, 2009, Platform talk with
abstract only.
38. K. M. Carter, R. Raich, and A. O. Hero, “An information
geometric approach to supervised dimensionality reduction,” in Proc.
IEEE Intl. Conf. Acoust., Speech, Signal Processing, Taipei, Taiwan,
2009, pp. 1829–1832.
37. K. M. Carter, C. K-M Kim, R. Raich, and A. O. Hero, “Information
preserving embeddings for discrimination,” in proc. of IEEE 13th
Digital Signal Processing Workshop, Marco Island, FL, 2009, pp.
381–386.
36. A. Mody, R. Raich, and G. L. St¨uber, “Joint iterative parameter
estimation from the cram´er-rao lower bound for an OFDM system,” in
Proc. IEEE Military Communications Conference, San Diego, CA, 2008,
pp. 1–7.
35. M. Thangavelu and R. Raich, “Multiclass linear dimension
reduction via a generalized Chernoff bound,” in Proc. IEEE
International Workshop on Machine Learning for Signal Processing,
Canc´un, Mexico, 2008, pp. 350–355.
34. Kevin M. Carter, Raviv Raich, William G. Finn, and Alfred O.
Hero III, “Dimensionality reduction of flow cytometric data through
information preservation,” in Proc. IEEE International Workshop on
Machine Learning for Signal Processing, Canc´un, Mexico, 2008, pp.
462–467.
33. H. Ba˘gcı, R. Raich, A. O. Hero, and E. Michielssen,
“Sparsity-regularized born iterations for electromagnetic inverse
scattering,” in Proc. IEEE Antennas and Propagation Society
International Symposium, San Diego, CA, 2008, pp. 1–4.
32. Raghuram Rangarajan, Raviv Raich, and Alfred O. Hero, “Euclidean
matrix completion problems in tracking and geo-localization,” in
Proc. IEEE Intl. Conf. Acoust., Speech, Signal Processing, Las
Vegas, NV, 2008, pp. 5324 – 5327.
31. Kyle Herrity, Raviv Raich, and Alfred O. Hero, “Blind
deconvolution for sparse molecular imaging,” in Proc. IEEE Intl.
Conf. Acoust., Speech, Signal Processing, Las Vegas, NV, 2008, pp.
545 – 548.
30. Kevin M. Carter, Raviv Raich, and Alfred O. Hero III, “FINE:
information embedding for document classification,” in Proc. IEEE
Intl. Conf. Acoust., Speech, Signal Processing, Las Vegas, NV, 2008,
pp. 1861 – 1864.
29. Kyle Herrity, Raviv Raich, and Alfred O. Hero, “Blind
reconstruction of sparse images with unknown point spread function,”
in Proc. Computational Imaging Conference in IS&T/SPIE Symposium
on Electronic Imaging Science and Technology, San Jose, CA, Jan.
2008, vol. 6814, p. 68140K.
28. Kevin M. Carter, Raviv Raich, and Alfred O. Hero III, “Learning
on manifolds for clustering and visualization,” in Proc. Allerton
Conf. on Communication, Control, and Computing, Monticello, IL,
Sept. 2007.
27. R. Rangarajan, R. Raich, and A.O. Hero, “Blind tracking using
sparsity penalized multidimensional scaling,” in Proc. 14th IEEE
Signal Processing Workshop on Statistical Signal and Array
Processing, Aug. 2007, pp. 670–674.
26. K. M. Carter, A. O. Hero III, and R. Raich, “De-biasing local
dimension estimation,” in Proc. 14th IEEE Signal Processing Workshop
on Statistical Signal and Array Processing, Aug. 2007, pp. 601–605.
25. R. Rangarajan, R. Raich, and A. O. Hero III, “Sequential energy
allocation strategies for channel estimation,” in Proc. IEEE Intl.
Conf. Acoust., Speech, Signal Processing, Honolulu, Hawaii, April
2007, vol. 3, pp. 821–824.
24. J. A. Marble, R. Raich, and A.O. Hero, “Iterative redeployment
of illumination and sensing (IRIS): Application to STW-SAR imaging,”
in Proc. of 25th Army Science Conference, Orlando, FL, Nov. 2006.
23. M. Ting, R. Raich, and A. O. Hero III, “Sparse image
reconstruction using a sparse prior,” in Proc. IEEE Int. Conf. Image
Processing, Atlanta, GA, Oct. 2006, pp. 1261–1264.
22. R. Raich and A. O. Hero III, “Sparse image reconstruction for
partially unknown blur functions,” in Proc. IEEE Int. Conf. Image
Processing, Atlanta, GA, Oct. 2006, pp. 637–640.
21. R. Rangarajan, R. Raich, and A. O. Hero III, “Single-stage
waveform selection for adaptive resource constrained state
estimation,” in Proc. IEEE Intl. Conf. Acoust., Speech, Signal
Processing, Toulouse, France, May 2006, vol. 3, pp. 672–675.
20. R. Raich, J. A. Costa, and A. O. Hero III, “On dimensionality
reduction for classification and its application,” in Proc. IEEE
Intl. Conf. Acoust., Speech, Signal Processing, Toulouse, France,
May 2006, vol. 5, pp. 917–920.
19. R. Rangarajan, R. Raich, and A. O. Hero III, “Sequential design
of experiments for a Rayleigh inverse scattering problem,” in
IEEE/SP 13th Workshop on Statistical Signal Processing, Bordeaux,
France, July 2005, pp. 625–630.
18. R. Rangarajan, R. Raich, and A. O. Hero III, “Optimal
experimental design for an inverse scattering problem,” in Proc.
IEEE Intl. Conf. Acoust., Speech, Signal Processing, Philadelphia,
PA, March 2005, vol. 4, pp. 1117–1120.
17. H. Qian, R. Raich, and G. T. Zhou, “Optimization of SNDR in the
presence of amplitude limited nonlinearity and multipath fading,” in
Proc. 38th IEEE Asilomar Conference on Signals, Systems and
Computers, Pacific Grove, CA, Nov. 2004, vol. 1, pp. 712–716.
16. R. Raich, H. Qian, and G. T. Zhou, “Signal to noise and
distortion ratio considerations for nonlinear communication
channels,” in Proc. IEEE 6th CAS Symposium on Emerging Technologies:
Frontiers of Mobile and Wireless Communication, Shanghai, China, May
2004.
15. H. Qian, R. Raich, and G. T. Zhou, “On the benefits of
deliberately introduced baseband nonlinearities in communication
systems,” in Proc. IEEE Intl. Conf. Acoust., Speech, Signal
Processing, Montr´eal, Canada, May 2004, vol. 2, pp. 905–908.
14. R. Raich and G. T. Zhou, “Spectral analysis for bandpass
nonlinearity with cyclostationary input,” in Proc. IEEE Intl. Conf.
Acoust., Speech, Signal Processing, Montr´eal, Canada, May 2004,
vol. 2, pp. 465–468.
13. C. Xiao, R. Raich, and G. T. Zhou, “QoS constrained statistical
resource reservation for wireless networks,” in Proc. 37th IEEE
Asilomar Conference on Signals, Systems and Computers, Pacific
Grove, CA, Nov. 2003, vol. 2, pp. 1713–1717.
12. R. Raich and G. T. Zhou, “Theory and applications of orthogonal
polynomials for Gaussian input,” in Proc. IEEE Workshop on
Statistical Signal Processing, St. Louis, MO, Sept. 2003, pp.
97–100.
11. G. T. Zhou and R. Raich, “Closed-form expressions of output
power spectrum for nonlinear power amplifiers with or without
memory,” in Proc. IEEE - EURASIP Workshop on Nonlinear Signal and
Image Processing, Trieste, Italy, June 2003.
10. R. Raich, H. Qian, and G. T. Zhou, “Digital baseband
predistortion of nonlinear power amplifiers using orthogonal
polynomials,” in Proc. IEEE Intl. Conf. Acoust., Speech, Signal
Processing, Hong Kong, China, Apr. 2003, vol. 6, pp. 689–692.
9. R. Raich and G. T. Zhou, “On the modeling of memory nonlinear
effects of power amplifiers for communication applications,” in
Proc. 10th IEEE DSP Workshop, Pine Mountain, GA, Oct. 2002, pp.
7–10.
8. Y. C. Park, W. Woo, R. Raich, J. S. Kenney, and G. T. Zhou,
“Adaptive predistortion linearization of RF power amplifiers using
lookup tables generated from subsampled data,” in Proc. Radio and
Wireless Conference, Boston, MA, Aug. 2002, pp. 233–236.
7. Lei Ding, Raviv Raich, and G. Tong Zhou, “A Hammerstein
predistortion linearization design based on the indirect learning
architecture,” in Proc. IEEE Intl. Conf. Acoust., Speech, Signal
Processing, Orlando, FL, May 2002, vol. 3, pp. 2689–2692.
6. J. S. Kenney, W. Woo, L. Ding, R. Raich, H. Ku, and G. T. Zhou,
“The impact of memory effects on predistortion linearization of RF
power amplifiers,” in Proc. 8th International Symposium on Microwave
and Optical Technology, Qu´ebec, Canada, June 2001, pp. 189–193.
5. J. Friedmann, R. Raich, J. Goldberg, and H. Messer, “Performance
analysis of mis-modeled estimation procedures for a distributed
source of non-constant modulus,” in Proc. 11th IEEE Workshop on
Statistical Signal Processing, Orchid Country Club, Singapore, Aug.
2001, pp. 221–224.
4. R. Raich and T. Zhou, “Analyzing spectral regrowth of QPSK and
OQPSK signals,” in Proc. IEEE Intl. Conf. Acoust., Speech, Signal
Processing, Salt Lake City, UT, May 2001, vol. 4, pp. 2673–2676.
3. R. G. Vaughan, P. Teal, and R. Raich, “Short term mobile channel
prediction using discrete scatterer propagation model and subspace
signal processing algorithms,” in Proc. IEEE Vehicular Technology
Conference, Boston, MA, Sept. 2000, vol. 2, pp. 751–758.
2. R. Raich, J. Goldberg, and H. Messer, “Localization of a
distributed source which is ‘partially coherent’ - modeling and
Cram´er Rao bounds,” in Proc. IEEE Intl. Conf. Acoust., Speech,
Signal Processing, Phoenix, AZ, Mar. 1999, vol. 5, pp. 2941–2944.
1. R. Raich, J. Goldberg, and H. Messer, “Bearing estimation for a
distributed source via the conventional beamformer,” in Proc. 9th
IEEE Signal Processing Workshop on Statistical Signal and Array
Processing, Portland, OR, Sept. 1998, pp. 5–8.
Last updated Jun. 29, 2018.
Raviv Raich, assistant professor, department of EECS, Oregon
State University