Raviv
Raich |

Home
Research
Teaching Research Group
Publications |

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**

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.

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, Accepted.

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 (

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,

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.