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author:

Cai, Jinyu (Cai, Jinyu.) [1] | Wang, Shiping (Wang, Shiping.) [2] | Xu, Chaoyang (Xu, Chaoyang.) [3] | Guo, Wenzhong (Guo, Wenzhong.) [4]

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EI

Abstract:

Deep clustering aims to promote clustering tasks by combining deep learning and clustering together to learn the clustering-oriented representation, and many approaches have shown their validity. However, the feature learning modules in existing methods hardly learn a discriminative representation. In addition, the label assignment mechanism becomes inefficient when dealing with some hard samples. To address these issues, a new joint optimization clustering framework is proposed through introducing the contractive representation in feature learning and utilizing focal loss in the clustering layer. The contractive penalty term added in feature learning would cause the local feature space contraction, resulting in learning more discriminative features. To our certain knowledge, this is also the first work to utilize the focal loss to improve the label assignment in deep clustering method. Moreover, the construction of the joint optimization framework enables the proposed method to learn feature representation and label assignment simultaneously in an end-to-end way. Finally, we comprehensively compare with some state-of-the-art clustering approaches on several clustering tasks to demonstrate the effectiveness of the proposed method. © 2021

Keyword:

Deep learning Signal encoding

Community:

  • [ 1 ] [Cai, Jinyu]College of Computer and Data Science, Fuzhou University, Fuzhou; 350116, China
  • [ 2 ] [Cai, Jinyu]Network Computing and Intelligent Information Processing Laboratory, Fuzhou University, Fuzhou; 350116, China
  • [ 3 ] [Wang, Shiping]College of Computer and Data Science, Fuzhou University, Fuzhou; 350116, China
  • [ 4 ] [Wang, Shiping]Network Computing and Intelligent Information Processing Laboratory, Fuzhou University, Fuzhou; 350116, China
  • [ 5 ] [Xu, Chaoyang]School of Information Engineering, Putian University, Putian; 351100, China
  • [ 6 ] [Guo, Wenzhong]College of Computer and Data Science, Fuzhou University, Fuzhou; 350116, China
  • [ 7 ] [Guo, Wenzhong]Network Computing and Intelligent Information Processing Laboratory, Fuzhou University, Fuzhou; 350116, China

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Source :

Pattern Recognition

ISSN: 0031-3203

Year: 2022

Volume: 123

8 . 0

JCR@2022

7 . 5 0 0

JCR@2023

ESI HC Threshold:66

JCR Journal Grade:1

CAS Journal Grade:1

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count: 45

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 1

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