Content Gazette - May 2021 - 12

APRIL 2021

VOLUME 15

NUMBER 3

IJSTGY

(ISSN 1932-4553)

ISSUE ON TENSOR DECOMPOSITION FOR SIGNAL PROCESSING AND MACHINE LEARNING
EDITORIAL

Introduction to the Special Issue on Tensor Decomposition for Signal Processing and Machine Learning . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . H. Chen, S. A. Vorobyov, H. C. So, F. Ahmad, and F. Porikli

433

SPECIAL ISSUE PAPERS

Tensor Decompositions in Wireless Communications and MIMO Radar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . H. Chen, F. Ahmad, S. Vorobyov, and F. Porikli
Adaptive Rank Selection for Tensor Ring Decomposition . . . . . . . . . . . . . . . . . . . . F. Sedighin, A. Cichocki, and A.-H. Phan
Block-Term Tensor Decomposition: Model Selection and Computation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. A. Rontogiannis, E. Kofidis, and P. V. Giampouras
Tensor Decomposition Learning for Compression of Multidimensional Signals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. Aidini, G. Tsagkatakis, and P. Tsakalides
Inexact Generalized Gauss-Newton for Scaling the Canonical Polyadic Decomposition With Non-Least-Squares Cost
Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . M. Vandecappelle, N. Vervliet, and L. D. Lathauwer
A Flexible Optimization Framework for Regularized Matrix-Tensor Factorizations With Linear Couplings . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C. Schenker, J. E. Cohen, and E. Acar
Tensor Recovery via ∗L - Spectral k-Support Norm . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. Wang, G. Zhou, Z. Jin, and Q. Zhao
Prema: Principled Tensor Data Recovery From Multiple Aggregated Views . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . F. M. Almutairi, C. I. Kanatsoulis, and N. D. Sidiropoulos
Krylov-Levenberg-Marquardt Algorithm for Structured Tucker Tensor Decompositions . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . P. Tichavsk´y, A.-H. Phan, and A. Cichocki
Bayesian Allocation Model: Marginal Likelihood-Based Model Selection for Count Tensors . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . S. Yıldırım, M. B. Kurutmaz, M. Barsbey, U. S¸ims¸ekli, and A. T. Cemgil
Fast Search of the Optimal Contraction Sequence in Tensor Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . L. Liang, J. Xu, L. Deng, M. Yan, X. Hu, Z. Zhang, G. Li, and Y. Xie
Dynamic L1-Norm Tucker Tensor Decomposition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . D. G. Chachlakis, M. Dhanaraj, A. Prater-Bennette, and P. P. Markopoulos
Deep Convolutional Neural Network Compression via Coupled Tensor Decomposition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . W. Sun, S. Chen, L. Huang, H. C. So, and M. Xie
Tensor-Based Reinforcement Learning for Network Routing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . K.-C. Tsai, Z. Zhuang, R. Lent, J. Wang, Q. Qi, L.-C. Wang, and Z. Han
Tensor Dropout for Robust Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . A. Kolbeinsson, J. Kossaifi, Y. Panagakis, A. Bulat, A. Anandkumar, I. Tzoulaki, and P. M. Matthews

438
454
464
476
491
506
522
535
550
560
574
587
603
617
630

(Contents Continued on Page 432)

www.signalprocessingsociety.org

[12]

MAY 2021


https://ieeexplore.ieee.org/document/9345960 https://ieeexplore.ieee.org/document/9393485 https://ieeexplore.ieee.org/document/9393485 https://ieeexplore.ieee.org/document/9321501 https://ieeexplore.ieee.org/document/9321500 https://ieeexplore.ieee.org/document/9335033 https://ieeexplore.ieee.org/document/9298833 https://ieeexplore.ieee.org/document/9298877 https://ieeexplore.ieee.org/document/9353206 https://ieeexplore.ieee.org/document/9345978 https://ieeexplore.ieee.org/document/9354901 https://ieeexplore.ieee.org/document/9296951 https://ieeexplore.ieee.org/document/9325533 https://ieeexplore.ieee.org/document/9358012 https://ieeexplore.ieee.org/document/9261106 https://ieeexplore.ieee.org/document/9343680 https://ieeexplore.ieee.org/document/9381098 http://www.signalprocessingsociety.org

Content Gazette - May 2021

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