Conference Papers - Signal Processing

C49. D.G. Wolnick, S. Ibrahim, T. Marrinan, and X. Fu ‘‘Deep Learning from Noisy Labels via Robust Nonnegative Matrix Factorization-Based Design" accepted to IEEE CAMSAP 2023.

C48. M. Ding, X. Fu, and X.-L. Zhao, ‘‘Bilinear Hyperspectral Unmixing via Tensor Decomposition’’ EUSIPCO 2023, Helsinki, Finland

C47. S. Shrestha, X. Fu, and M. Hong, ‘‘Towards Efficient and Optimal Joint Beamforming and Antenna Selection: A Machine Learning Approach ’’, ICASSP 2023, Greece

C46. M. Shao and X. Fu, “Massive MIMO Channel Estimation via Compressed and Quantized Feedback,” 2022 56th Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, USA, 2022, pp. 1016-1020, doi: 10.1109/IEEECONF56349.2022.10052071.

C45. S. Timilsina, S. Shrestha, and X. Fu, ‘‘Deep Spectrum Cartography from Quantized Measurements’’, ICASSP 2023, Greece

C44. T. Nguyen, X. Fu, and R. Wu. “Memory-efficient convex optimization for self-dictionary nonnegative matrix factorization.” 2022 56th Asilomar Conference on Signals, Systems, and Computers. IEEE, 2022.

C43. S. Shrestha and X. Fu ‘‘Communication-efficient distributed generalized CCA via error feedback-assisted quantization’’, ICASSP 2022, Singapore.

C42. L. Huang, C. Deng, S. Ibrahim, X Fu, and B. Yuan, ‘‘VLSI hardware architecture of stochastic low-rank tensor decomposition’’, Asilomar 2022, Pacific Grove, CA, USA.

C41 M. Ding, X. Fu, T.-Z. Huang, and X.-L. Zhao ‘‘Constrained Block-Term Tensor Decomposition-Based Hyperspectral Unmixing via Alternating Gradient Projection’’, EUSIPCO 2021, Ireland.

C40. W. Pu, S. Ibrahim, X. Fu and M. Hong, ‘‘Fiber-Sampled Stochastic Mirror Descent For Tensor Decomposition with beta-Divergence’’, IEEE ICASSP 2021, Toronto, Canada.

C39. H. Sun, W. Pu, M. Zhu, X. Fu, T.-H. Chang, and M. Hong. ‘‘Learning to Continuously Optimize Wireless Resource In Episodically Dynamic Environment’’ IEEE ICASSP 2021, Toronto, Canada.

C38. S. Shrestha, X. Fu, and M. Hong ‘‘Deep Generative Model Learning for Blind Spectrum Cartography with NMF-based Radio Map Disaggregation’’ IEEE ICASSP 2021, Toronto, Canada.

C37. S. Ibrahim and X. Fu, ‘‘Leaning Mixed Membership from Adjacency Graph via Systematic Edge Query: Identifiability and Algorithm’’ IEEE ICASSP 2021, Toronto, Canada.

C36. Q. Lyu and X. Fu, ‘‘Nonlinear Multiview Analysis: Identifiability and Neural Network-based implementation’’, IEEE SAM 2020, June 8-12, Hangzhou, China. (Best Student Paper Finalist) .

C35. S. Ibrahim and X. Fu, ‘‘Recovering Joint PMF from Pairwise Marginals,’’ submitted to Asilomar 2020, May, 2020

C34. Q. Lyu and X. Fu, ‘‘Nonlinear dependent component analysis: Identifiability and algorithm’’, EUSIPCO 2020, accepted.

C33. K. Huang and X. Fu, “Proximal Gauss-Newton based nonnegative matrix factorization”, IEEE GlobalSIP 2019.

C32. T. Vu, R. Raich and X. Fu, “On convergence of projected gradient descent for minimizing a large-scale quadratic over the unit sphere”, IEEE MLSP 2019. (won an IEEE MLSP 2019 Best Student Paper Award) .

C31. X. Fu and K. Huang, “Block-term tensor decomposition via structured matrix factorization”, IEEE MLSP 2019.

C30. C. Qian, X. Fu, and N. D. Sidiropoulos, ‘‘A Simple Algebraic Channel Estimation Method for FDD Massive MIMO systems’’, IEEE SPAWC 2019.

C29. C.I. Kanatsoulis, N. D. Sidiropoulos, M. Akcakaya, and X. Fu ‘‘Regular sampling of tensor signals: Theory and application to fMRI’’, IEEE ICASSP 2019, Brighton, UK, May, 2019.

C28. X. Fu, C. Gao, H.-T. Wai and K. Huang, ‘‘Block-randomized stochastic proximal gradient for large-scale tensor factorization’’, IEEE ICASSP 2019, Brighton, UK, May, 2019.

C27. S. Ibrahim and X. Fu, ‘‘Stochastic optimization for coupled tensor decomposition with appli- cations in statistical learning’’, IEEE DSW 2019, Minneapolis, U.S. June 2109.

C26. R. Wu, Q. Li, X. Fu, and W.-K. Ma, ‘‘A Convex Low-Rank Regularization Method For Hyperspectral Super-Resolution’’, 2018 IEEE Statistical Signal Processing Workshop (SSP), accepted.

C25. M. Salah, A. S. Zamzam, X. Fu, and N. D. Sidiropoulos, ‘‘Learning-Based Antenna Selection for Multicasting’’, IEEE SPAWC 2018.

C24. H. Sun, Z. Zhao, X. Fu, and M. Hong, ‘‘Limited Feedback Double Directional Massive MIMO Channel Estimation: From Low-Rank Modeling to Deep Learning’’, IEEE SPAWC 2018.

C23. C. Kanatsoulis, X. Fu, N. D. Sidiropoulos, and W.-K. Ma, ‘‘Hyperspectral Super-resolution: combining low-rank tensor and matrix structure’’, IEEE ICIP 2018, accepted.

C22. Y. Shen, X. Fu, G. B. Giannakis, and N. D. Sidiropoulos, “Inferring Directed Network Topologies via Joint Diagonalization,” Proc. of Asilomar Conf., Pacific Grove, CA, Oct. 29 - Nov. 1, 2017.

C21. C. Qian, X. Fu, N. D. Sidiropoulos, and Y. Yang, ‘‘Tensor-based parameter estimation of double directional massive MIMO channel with dual-polarized antennas’’, ICASSP 2018, Calgary, Canada, April 15-20, 2018.

C20. H. Dantas, J. Nieven, T. S. Davis, X. Fu, G. A. Clark, D. J. Warren, and V J. Mathews, ‘‘Shared human-machine control for self-aware prothesis’’, ICASSP 2018, Calgary, Canada, April 15-20, 2018.

C19. C. Kanatsoulis, X. Fu, N. D. Sidiropoulos, and W.-K. Ma, ‘‘Hyperspectral super-resolution via coupled tensor factorization: Identibability and algorithms’’, ICASSP 2018, Calgary, Canada, April 15-20, 2018.

C18. C. Katantsoulis, X. Fu, N. D. Sidiropoulos, and M. Hong, ‘‘Large-scale regularized SUMCOR GCCA via penalty-dual decomposition’’, ICASSP 2018, Calgary, Canada, April 15-20, 2018.

C17. R.Wu, M. Chan, H.-T.Wai, W.-K. Ma, and X. Fu, ‘‘Hi, BCD! Hybrid block coordiante descent for hyperspectral super-resolution’’, ICASSP 2018, Calgary, Canada, April 15-20, 2018.

C16. H. Sun, X. Chen, Q. Shi, M. Hong, X. Fu, and N. D. Sidiropoulos, ‘‘Learning to optimize: training deep neural networks for wireless resource management’’,in Proc. IEEE SPAWC 2017, Sapporo, Japan, Jul. 3–6, 2017

C15. P. N. Alevizos, X. Fu, N. D. Sidiropoulos, Y. Yang, and A. Bletsas, ‘‘Non-uniform Directional Dictionary-Based Limited Feedback for Massive MIMO Systems’’, IEEE WiOPT 2017, to appear.

C14. A. S.Zamzam, X. Fu, E. Dall‘Anese, and N. D. Sidiropoulos,‘‘Distributed optimal power flow using feasible point pursuit’’, IEEE CAMSAP 2017.

C13. R. Wu, W.-K. Ma, and X. Fu, ‘‘A stochastic maximum-likelihood framework for simplex structured matrix factorization’’, ICASSP 2017, to appear.

C12. X. Fu, K. Huang, M. Hong, N. D. Sidiropoulos, and A. M.-C. So, ‘‘Scalable and flexible MAX-VAR generalized canonical correlation analysis via alternating optimization’’, ICASSP 2017, to appear.

C11. C. Qian, X. Fu, N.D. Sidiropoulos, L. Huang, ‘‘Inexact alternating optimization for phase retrieval with outliers’’, EUSIPCO2016.

C10. J. Tranter, N.D. Sidiropoulos, X. Fu, A. Swami, ‘‘Fast unit-modulus least squares with applications in transmit beaforming’’, EUSIPCO2016.

C9. X. Fu, W.-K. Ma, K. Huang, and N. D. Sidiropoulos, ‘‘Robust volume minimization-based structured matrix factorization via alternating optimization’’, IEEE ICASSP 2016, to appear.

C8. B. Yang, X. Fu, N. D. Sidiropoulos, ‘‘Joint Factor Analysis and Latent Clustering’’, IEEE CAMSAP 2015, Cancun, Mexico, Dec. 2015 (won a Best Student Paper Award) .

C7. X. Fu, W.-K. Ma, J. M. Bioucas-Dias, and T.-H. Chan, “A robust method for semiblind dictionary-aided hyperspectral unmixing,” in Proc. WHISPERS 2014, Lausanne, Switzerland, Jun. 2014.

C6. X. Fu, N. D. Sidiropoulos, and W.-K. Ma, “Tensor-based power spectra separation and emitter localization for cognitive radio,” in Proc. SAM 2014, A Coru?a, Spain Jun. 2014(finalist of SAM2014 Best Student Paper Competition) .

C5. X. Fu, N. D. Sidiropoulos, W.-K. Ma, and J. Tranter, “Blind spectra separation and direction finding for cognitive radio using temporal correlation-domain ESPRIT,” in Proc. ICASSP 2014, Florence, Italy, May 2014 (won an ICASSP2014 Best Student Paper Award) .

C4. X. Fu, W.-K. Ma, T.-H. Chan, J. M. Bioucas-Dias, and M.-D. Iordache, “Greedy algorithms for pure pixel identification in hyperspectral unmixing: A multiple-measurement vector viewpoint,” in Proc. EUSIPCO, Marrakech, Morocco, Sep 2013.

C3. X. Fu and W.-K. Ma, “Blind separation of convolutive mixtures of speech sources: Exploiting local sparsity,” in Proc. ICASSP, Vancouver, Canada, May 2013.

C2. X. Fu, W.-K. Ma, F. W. K. Chan and H. C. So, “A complex-valued semidefinite relaxation approach for two-dimensional source localization using distance measurements and imperfect receiver positions,” in Proc. International Conference on Signal Processing, Beijing, China, 2012.

C1. X. Fu and W.-K. Ma, “A simple closed-form solution for overdetermined blind separation of locally sparse quasi-stationary sources,” in Proc. IEEE ICASSP 2012, Kyoto, Japan, Mar 25-30, 2012.

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