Journal Articles

Pre-Prints

Accepted/Published

J56. Y.-C. Miao, X.-L. Zhao, J.-L. Wang, X. Fu, Y. Wang, ‘‘Snapshot Compressive Imaging Using Domain-Factorized Deep Video Prior’’, IEEE Transactions on Computational Imaging, accepted, Dec. 2023

J55. S. Timilsina, S. Shrestha, and X. Fu, ‘‘Quantized Radio Map Estimation Using Tensor and Deep Generative Models’’, IEEE Transactions on Signal Processing, accepted, Nov. 2023 (Our Simulation Code (including a radio map generator))

J54. T. Vu, R. Raich, X. Fu, ‘‘On Local Linear Convergence of Projected Gradient Descent for Unit-Modulus Least Squares’’ , IEEE Transactions on Signal Processing, accepted, Oct. 2023

J53. S. Shrestha and X. Fu, ‘‘Communication-Efficient Federated Linear and Deep Generalized Canonical Correlation Analysis’’, IEEE Transactions on Signal Processing, accepted, Mar. 2023

J52. S. Shrestha, X. Fu, and M. Hong, ‘‘Optimal Solutions for Joint Beamforming and Antenna Selection: From Branch and Bound to Graph Neural Imitation Learning’’, IEEE Transactions on Signal Processing, accepted, Jan. 2023 (Source Code)

J51. M. Ding, X. Fu, and X.-L. Zhao ‘‘Fast and Structured Block-Term Tensor Decomposition for Hyperspectral Unmixing’’, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, accepted, Jan 2023 (Source Code)

J50. T. Nguyen, X. Fu, and R. Wu ‘‘Memory-Efficient Convex Optimization for Self-Dictionary Separable Nonnegative Matrix Factorization: A Frank-Wolfe Approach’’, IEEE Transactions on Signal Processing, accepted, May 2022.

J49. W. Pu, S. Ibrahim, X. Fu, and M. Hong, ‘‘Stochastic Mirror Descent for Low-Rank Tensor Decomposition Under Non-Euclidean Losses’’, IEEE Transactions on Signal Processing, accepted, April, 2022. (Source Code)

J48. Q. Lyu and X. Fu, ‘‘Finite-Sample Analysis of Deep CCA-Based Unsupervised Post-Nonlinear Multimodal Learning’’, IEEE Transactions on Neural Networks and Learning Systems , accepted, Mar. 2022 (Code of The Algorithm from J36)

J47. S. Shrestha, X. Fu, and M. Hong, ‘‘Deep Spectrum Cartography: Completing Radio Map Tensors Using Learned Neural Models’’, IEEE Transactions on Signal Processing, Jan, 2022. (Source Code)

J46. H. Sun, W. Pu, X. Fu, T.-H. Chang, and M. Hong, ‘‘Learning to Continuously Optimize Wireless Resource in a Dynamic Environment: A Bilevel Optimization Perspective’’, IEEE Transactions on Signal Processing, Jan, 2022. (Source Code)

J45. S. Ibrahim and X. Fu, ‘‘Mixed membership graph clustering via systematic edge query’’, IEEE Transactions on Signal Processing, accepted, Aug. 2021.

J44. Y.-C. Miao, X.-L. Zhao, X. Fu, J.-L. Wang, and Y.-B. Zheng, ‘‘Hyperspectral Denoising Using Unsupervised Disentangled Spatio-Spectral Deep Priors’’, IEEE Transactions on Geoscience and Remote Sensing, accepted, Aug. 2021.

J43. Q. Lyu and X. Fu, ‘‘Identifiability-Guaranteed Simplex-Structured Post-Nonlinear Mixture Learning via Autoencoder’’, IEEE Transactions on Signal Processing, vol. 69, pp. 4921-4936, 2021. (Source Code)

J42. S. Ibrahim and X. Fu, ‘‘Recovering joint probability of discrete random variables from pairwise marginals’’, IEEE Transactions on Signal Processing, vol. 69, pp. 4116-4131, 2021. (Source Code)

J41. M. Ding, X. Fu, T.-Z. Huang, J. Wang, and X.-L. Zhao, ‘‘Hyperspectral super-resolution via interpretable block-term tensor modeling’’, IEEE Journal of Selected Topics in Signal Processing,vol. 15, no. 3, pp. 641-656, April 2021. (Source Code)

J40. X. Fu, N. Vervliet, L. De Lathauwer, K. Huang and N. Gillis, ‘‘Computing Large-Scale Matrix and Tensor Decomposition With Structured Factors: A Unified Nonconvex Optimization Perspective’’, IEEE Signal Processing Magazine, special issue on ‘‘Non-Convex Optimization for Signal Processing and Machine Learning’’, vol. 37, no. 5, pp. 78-94, Sept. 2020.

J39. Q. Shi, M. Hong, X. Fu, and T.-H. Chang, ‘‘Penalty Dual Decomposition Method for Nonsmooth Nonconvex Optimization-Part II: Applications," in IEEE Transactions on Signal Processing, vol. 68, pp. 4242-4257, 2020.

J38. G. Zhang, X. Fu, J. Wang, X.-L. Zhao, and M. Hong, ‘‘Spectrum Cartography via Coupled Block-Term Tensor Decomposition’’, IEEE Transactions on Signal Processing, vol. 68, pp. 3660-3675, 2020. (Source Code)

J37. S. Ibrahim, X. Fu, and X. Li, ‘‘On recoverability of randomly compressed tensors with low CP rank’’, IEEE Signal Processing Letters, vol. 27, pp. 1125-1129, 2020.

J36. Q. Lyu and X. Fu, ‘‘Nonlinear Multiview Analysis: Identifiability and Neural Network-Assisted Implementation’’ IEEE Transactions on Signal Processing, vol. 68, pp. 2697-2712, 2020. (Source Code)

J35. Y. Shen, X. Fu, G. B. Giannakis, and N. D. Sidiropoulos, ‘‘Topology Identification of Directed Graphs via Joint Diagonalization of Correlation Matrices,’’ IEEE Transactions on Signal and Information Processing over Networks, Special Issue on Network Topology Inference, vol. 6, pp. 271-283, 2020.

J34. X. Fu, S. Ibrahim, H.-T. Wai, C. Gao, and K. Huang, ‘‘Block-Randomized Stochastic Proximal Gradient for Low-Rank Tensor Factorization’’, IEEE Transactions on Signal Processing, vol. 68, pp. 2170-2185, 2020. (Matlab Code)

J33. K. Tang, N. Kan, J. Zou, C. Li, X. Fu, M. Hong, H. Xiong ‘‘Multi-user Adaptive Video Delivery over Wireless Networks: A Physical Layer Resource-Aware Deep Reinforcement Learning Approach’’, IEEE Transactions on Circuits and Systems for Video Technology, vol. 31, no. 2, pp. 798-815, Feb. 2021.

J32. R. Wu, W.-K. Ma, X. Fu and Q. Li, ‘‘Hyperspectral Super-Resolution via Global-Local Low-Rank Matrix Estimation’’, IEEE Transactions on Geoscience and Remote Sensing, vol. 58, no. 10, pp. 7125-7140, Oct. 2020.

J31. X. Fu, E. Seo, J. Clarke, and R. Hutchinson, ‘‘Link Prediction Under Imperfect Detection: Collaborative Filtering for Ecological Networks’’, IEEE Transactions on Knowledge and Data Engineering, vol. 33, no. 8, pp. 3117-3128, 1 Aug. 2021.

J30. B. Yang, X. Fu, N. D. Sidiropoulos, K. Huang, ‘‘Learning Nonlinear Mixtures: Identifiability and Algorithm’’, IEEE Transactions on Signal Processing, vol. 68, pp. 2857-2869, 2020.

J29. C. I. Kantatsoulis, X. Fu, N. D. Sidiropoulos, and M. Akcakaya‘‘Tensor Completion From Regular Sub-Nyquist Samples,’’ in IEEE Transactions on Signal Processing, vol. 68, pp. 1-16, 2020.

J28. C. Qian, X. Fu, and N.D. Sidiropoulos, ‘‘Amplitude Retrieval for Channel Estimation of MIMO Systems with One-Bit ADCs’’, IEEE Signal Processing Letters, vol. 26, no. 11, pp. 1698-1702, Nov. 2019.

J27. C. Qian, X. Fu, and N. D. Sidiropoulos, ‘‘Algebraic Channel Estimation Algorithms for FDD Massive MIMO systems’’, IEEE Journal of Selected Topics in Signal Processing, vol. 13, no. 5, pp. 961-973, Sept. 2019.

J26. A. S. Zamzam, X. Fu, N. D. Sidiropoulos, ‘‘Data-Driven Learning-Based Optimization for Distribution System State Estimation’’, IEEE Transactions on Power Systems, vol. 34, no. 6, pp. 4796-4805, Nov. 2019.

J25. X. Fu, K. Huang, N. D. Sidiropoulos, and W.-K. Ma, ‘‘Nonnegative Matrix Factorization for Signal and Data Analytics: Identifiability, Algorithms, and Applications’’, IEEE Signal Processing Magazine, feature article, vol. 36, no. 2, pp. 59-80, March 2019.

• • Slides of tutorial at SIAM Linear Algebra Conference 2021.

J24. C. I. Kantatsoulis, X. Fu, N. D. Sidiropoulos, and M. Hong, ‘‘Structured SUMCOR Multiview Canonical Correlation Analysis for Large-Scale Data’’, IEEE Transactions on Signal Processing, vol. 67, no. 2, pp. 306-319, 15 Jan.15, 2019.

J23., X. Fu , K. Huang, E.E. Papalexakis, H. Song, P. Talukdar, N. D. Sidiropoulos, C. Faloutsos, and T. Mitchell, ‘‘Efficient and Distributed Generalized Canonical Correlations Analysis for Big Multiview Data’’, IEEE Transactions on Knowledge and Data Engineering, vol. 31, no. 12, pp. 2304-2318, 1 Dec. 2019. (Matlab Demo)

• • Here is a python implementation of the algorithm with support to missing values; Thanks to Adrian Benton (Johns Hopkins University) for the implementation.

J22. C. Qian, X. Fu, N. D. Sidiropoulos, and Y. Yang, ‘‘Tensor-Based Channel Estimation for Dual-Polarized Massive MIMO Systems’’, IEEE Transactions on Signal Processing, vol. 66, no. 24, pp. 6390-6403, 15 Dec.15, 2018.

J21. C. I. Kantatsoulis, X. Fu, N. D. Sidiropoulos, and W.-K. Ma, ‘‘Hyperspectral super-resolution: A coupled tensor factorization approach’’, IEEE Transactions on Signal Processing, vol. 66, no. 24, pp. 6503-6517, 15 Dec.15, 2018.

J20. H. Sun, X. Chen, Q. Shi, M. Hong, X. Fu, and N.D. Sidiropoulos, ‘‘Learning to Optimize: Training Deep Neural Networks for Interference Management,’’ in IEEE Transactions on Signal Processing, vol. 66, no. 20, pp. 5438-5453, 15 Oct.15, 2018.

• • 2022 IEEE Signal Processing Society Best Paper Award

J19. P. N. Alevizos, X. Fu, N. D. Sidiropoulos, Y. Yang, and A. Bletsas, ‘‘Limited Feedback Channel Estimation in Massive MIMO with Non-uniform Directional Dictionaries’’, IEEE Transactions on Signal Processing, vol. 66, no. 19, pp. 5127-5141, 1 Oct.1, 2018.

J18. N. Kargas, N. D. Sidiropoulos, and X. Fu, ‘‘Tensors, Learning, and ‘‘Kolmogorov Extension’’ for Finite-alphabet Random Vectors,’’ in IEEE Transactions on Signal Processing, vol. 66, no. 18, pp. 4854-4868, 15 Sept.15, 2018.

J17. X. Fu*, K. Huang* (*equal contribution), N. D. Sidiropoulos, Q. Shi and M. Hong, ‘‘Anchor-free correlated topic modeling,’’ IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 41, no. 5, pp. 1056-1071, 1 May 2019.

J16. X. Fu, K. Huang, N. D. Sidiropoulos, ‘‘On Identifiability of Nonnegative Matrix Factorization,’’ IEEE Signal Processing Letters, vol. 25, no. 3, pp. 328-332, March 2018.

J15, T. Qiu, X. Fu, N. D. Sidiropoulos, and D. Palomar, ‘‘MISO Channel Estimation and Tracking from Received Signal Strength Feedback’’, IEEE Transactions on Signal Processing, vol. 66, no. 7, pp. 1691-1704, 1 April1, 2018.

J14. C. Qian, X. Fu, N. D. Sidiropoulos, L. Huang, and J. Xie, ‘‘Inexact alternating optimization for phase retrieval in the presence of outliers’’, IEEE Transactions on Signal Processing, vol. 65, no. 22, pp. 6069-6082, 15 Nov.15, 2017

J13. X. Fu, K. Huang, M. Hong, N. D. Sidiropoulos, and A.M.C. So, ‘‘Scalable and flexible MAX-VAR generalized canonical correlation analysis’’, IEEE Transactions on Signal Processing, vol. 65, no. 16, pp. 4150-4165, Aug.15, 15 2017. (Source Code)

J12. N.D. Sidiropoulos, L. De Lathauwer, X. Fu, K. Huang, E.E. Papalexakis, and C. Faloutsos, ‘‘Tensor Decomposition for Signal Processing and Machine Learning’’, overview article, IEEE Transactions on Signal Processing, vol. 65, no. 13, pp. 3551-3582, July 1, 2017.

• • 2022 IEEE Signal Processing Society Donald G. Fink Overview Paper Award

J11. B. Yang, X. Fu, N. D. Sidiropoulos, ‘‘Learning from hidden traits: Joint factor analysis and latent clustering’’, IEEE Transactions on Signal Processing, vol. 65, no. 1, pp. 256-269, Jan.1, 1 2017.

J10. X. Fu, K. Huang, B. Yang, W.-K. Ma, N. D. Sidiropoulos, ‘‘Robust volume minimization-based matrix factorization for remote sensing and document clustering’’, IEEE Transactions on Signal Processing, vol. 64, no. 23, pp. 6254-6268, Dec.1, 1 2016. (Matlab Demo)

J9. J. Tranter, N. D. Sidiropoulos, X. Fu, and A. Swami, “Fast unit-modulus least squares with applications in beamforming,” IEEE Transactions on Signal Processing, vol. 65, no. 11 pp. 2875-2887, June, 2017.

J8. X. Fu and W.-K. Ma, “Robustness analysis of structured matrix factorization via self-dictionary mixed-norm optimization,” IEEE Signal Processing Letters, vol. 23, no. 1, Jan. 2016.

J7. X. Fu, N. D. Sidiropoulos, and W.-K. Ma, “Power spectra separation via structured matrix factorization,” IEEE Transactions on Signal Processing, vol. 64, no. 17, pp. 4592-4605, Sept., 2016. Real Data (311 MB)

J6. X. Fu, W.-K. Ma, J. M. Bioucas-Dias, and T.-H. Chan, “Semiblind Hyperspectral Unmixing in the Presence of Spectral Library Mismatches,” IEEE Transactions on Geoscience and Remote Sensing, vol. 54, no. 9, pp. 5171-5184, Sept., 2016. Matlab Code: DANSER, RMUSIC, and Demo – L2/Lp-norm based (0<p<1) collaborative sparse unmixing of hyperspectral images.

J5. X. Fu, K. Huang, W.-K. Ma, N. D. Sidiropoulos, and R. Bro,“Joint tensor factorization and outlying slab suppression with applications,” IEEE Transactions on Signal Processing, vol. 63, no. 23, Dec. 2015. (Matlab Demo)

J4. X. Fu, N. D. Sidiropoulos, J. H. Tranter, and W.-K. Ma, “A factor analysis framework for power spectra separation and multiple emitter localization,” IEEE Transactions on Signal Processing, vol. 63, no. 24, Dec. 2015. (Matlab Code) Real Data (311 MB)

J3. X. Fu, W.-K. Ma, T.-H. Chan, and J. M. Bioucas-Dias, “Self-dictionary sparse regression for hyperspectral unmixing: Greedy pursuit and pure pixel search are related,” IEEE Journal of Selected Topics in Signal Processing, vol. 9, no. 6, Sep. 2015 (Matlab Demo)

J2. X. Fu, W.-K. Ma, K. Huang, and N. D. Sidiropoulos, “Blind separation of quasi-stationary sources: Exploiting convex geometry in covariance domain,” IEEE Transactions on Signal Processing, vol.63, no.9, pp.2306-2320, May, 2015. (Matlab Demo)

• • Here is an additional demo for the convoultive mixture model case: download here.

J1. K. K. Lee, W.-K. Ma, X. Fu, T.-H. Chan, and C.-Y. Chi, “A Khatri-Rao subspace approach to blind identification of mixtures of quasi-stationary sources,” Signal Processing, vol. 93, no. 12, pp. 3515-3527, Dec 2013 (special issue in memory of Alex B. Gershman). (Matlab Code)