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2020 IEEE Signal Processing Letters  
Amini 101 On the Identifiability of Transform Learning for Non-Negative Matrix Factorization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ...  Mahato 690 Negative Binomial Matrix Factorization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . O. Gouvert, T. Oberlin, and C.  ...  Bovik 2144 Low-Rank Regularized Deep Collaborative Matrix Factorization for Micro-Video Multi-Label Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ... 
doi:10.1109/lsp.2020.3040844 fatcat:xpovskhrvfgctk3hhufuvpyyne

2020 Index IEEE Signal Processing Letters Vol. 27

2020 IEEE Signal Processing Letters  
Kornilov, M.V., LSP 2020 1480-1484 Negative Binomial Matrix Factorization. Gouvert, O., +, LSP 2020 815-819 Non Zero Mean Adaptive Cosine Estimator and Application to Hyperspectral Imaging.  ...  Qi, J., +, LSP 2020 1485-1489 On the Identifiability of Transform Learning for Non-Negative Matrix Fac- torization.  ... 
doi:10.1109/lsp.2021.3055468 fatcat:wfdtkv6fmngihjdqultujzv4by

Preserving Ordinal Consensus: Towards Feature Selection for Unlabeled Data

Jun Guo, Heng Chang, Wenwu Zhu
2020 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
Specifically, we propose a compelling regularizer for SPL to obtain a robust loss. Finally, an alternating minimization algorithm is developed to efficiently optimize the proposed model.  ...  In the feature selection part, we design an ordinal consensus preserving term based on a triplet-induced graph.  ...  U T U = I, VV T = I, V ≥ 0, (1) where c is the number of latent clusters, all elements of V are non-negative. Hereafter, I denotes the identity matrix with a compatible size.  ... 
doi:10.1609/aaai.v34i01.5336 fatcat:3woh4jkl3fev3eunrivrgau3oq

Table of Contents

2020 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
Tao 738 Improved Collaborative Non-Negative Matrix Factorization and Total Variation for Hyperspectral Unmixing . (Contents Continued on Page x) (Contents Continued from Page ix) , and J.  ...  Gao 367 Adaptive Graph Regularized Multilayer Nonnegative Matrix Factorization for Hyperspectral Unmixing .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ...  Qian 5682 Ideal Regularized Discriminative Multiple Kernel Subspace Alignment for Domain Adaptation in Hyperspectral Image Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ... 
doi:10.1109/jstars.2020.3046663 fatcat:zqzyhnzacjfdjeejvzokfy4qze

2021 Index IEEE Transactions on Cybernetics Vol. 51

2021 IEEE Transactions on Cybernetics  
Shang, Y., TCYB Jan. 2021 318-331 Semisupervised Adaptive Symmetric Non-Negative Matrix Factorization.  ...  ., +, TCYB Sept. 2021 4567-4580 Uniform Distribution Non-Negative Matrix Factorization for Multiview Clustering.  ... 
doi:10.1109/tcyb.2021.3139447 fatcat:myjx3olwvfcfpgnwvbuujwzyoi

2020 Index IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Vol. 13

2020 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
., +, JSTARS 2020 5833-5846 Improved Collaborative Non-Negative Matrix Factorization and Total Vari- ation for Hyperspectral Unmixing.  ...  ., +, JSTARS 2020 5833-5846 Improved Collaborative Non-Negative Matrix Factorization and Total Vari- ation for Hyperspectral Unmixing.  ... 
doi:10.1109/jstars.2021.3050695 fatcat:ycd5qt66xrgqfewcr6ygsqcl2y

Table of Contents

2021 2021 40th Chinese Control Conference (CCC)   unpublished
LUO Haoyang, WANG Haikuan, ZHOU Wenju, LIU Kangli, FU Jingqi 7435 Nonnegative Matrix Factorization with Hypergraph Based on Discriminative Constraint and Nonsymmetric Reformulation . . . . . . . . . .  ...  DUAN Jufang, XU Xiangyang, WANG Yi 3403 An Improved Non-negative Latent Factor Model via Momentum-Based Additive Gradient Descent Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ... 
doi:10.23919/ccc52363.2021.9550117 fatcat:55y7a2gagfhtpc6llmfvl7gqpm