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[期刊论文]

Learning matrix factorization with scalable distance metric and regularizer

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author:

Wang, Shiping (Wang, Shiping.) [1] | Zhang, Yunhe (Zhang, Yunhe.) [2] | Lin, Xincan (Lin, Xincan.) [3] | Unfold

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Abstract:

Matrix factorization has always been an encouraging field, which attempts to extract discriminative features from high-dimensional data. However, it suffers from negative generalization ability and high computational complexity when handling large-scale data. In this paper, we propose a learnable deep matrix factorization via the projected gradient descent method, which learns multi-layer low-rank factors from scalable metric distances and flexible regularizers. Accordingly, solving a constrained matrix factorization problem is equivalently transformed into training a neural network with an appropriate activation function induced from the projection onto a feasible set. Distinct from other neural networks, the proposed method activates the connected weights not just the hidden layers. As a result, it is proved that the proposed method can learn several existing well-known matrix factorizations, including singular value decomposition, convex, nonnegative and semi-nonnegative matrix factorizations. Finally, comprehensive experiments demonstrate the superiority of the proposed method against other state-of-the-arts. © 2023 Elsevier Ltd

Keyword:

Clustering algorithms Deep learning Gradient methods Learning systems Matrix factorization Multilayer neural networks Signal encoding Singular value decomposition

Community:

  • [ 1 ] [Wang, Shiping]College of Computer and Data Science, Fuzhou University, Fuzhou; 350116, China
  • [ 2 ] [Wang, Shiping]Guangdong Provincial Key Laboratory of Big Data Computing, The Chinese University of Hong Kong, Shenzhen; 518172, China
  • [ 3 ] [Zhang, Yunhe]College of Computer and Data Science, Fuzhou University, Fuzhou; 350116, China
  • [ 4 ] [Lin, Xincan]College of Computer and Data Science, Fuzhou University, Fuzhou; 350116, China
  • [ 5 ] [Su, Lichao]College of Computer and Data Science, Fuzhou University, Fuzhou; 350116, China
  • [ 6 ] [Xiao, Guobao]College of Computer and Control Engineering, Minjiang University, Fuzhou; 350108, China
  • [ 7 ] [Zhu, William]Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu; 610054, China
  • [ 8 ] [Shi, Yiqing]College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou; 350117, China

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Source :

Neural Networks

ISSN: 0893-6080

Year: 2023

Volume: 161

Page: 254-266

6 . 0

JCR@2023

6 . 0 0 0

JCR@2023

ESI HC Threshold:32

JCR Journal Grade:1

CAS Journal Grade:2

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count: 4

30 Days PV: 1

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管理员  2024-11-18 17:06:48  更新被引

管理员  2024-10-25 14:35:46  追加

管理员  2024-09-25 15:11:56  追加

管理员  2024-09-17 09:44:38  更新被引

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