Deep boundary‑aware clustering by jointly optimizing unsupervised representation learning

Wang, Ru and Li, Lin and Wang, Peipei and Tao, Xiaohui ORCID: https://orcid.org/0000-0002-0020-077X and Liu, Peiyu (2022) Deep boundary‑aware clustering by jointly optimizing unsupervised representation learning. Multimedia Tools and Applications. pp. 1-16. ISSN 1380-7501


Abstract

Deep clustering obtains feature representation generally and then performs clustering for high dimension real-world data. However, conventional solutions are two-stage embedding learning-based methods and these two processes are separate and independent, which often leads to clustering results cannot feedback to optimize the representation learning and reduces the performance of deep clustering. In this paper, we aim to propose a deep boundary-aware clustering by jointly optimizing unsupervised representation learning. More specifically, we joint boundary-aware variational auto-encoder and deep regularized clustering for deep regularized clustering for unsupervised learning, named Boundary-aware DEep Clustering (BaDEC). BaDEC is able to learn feature representation and clustering simultaneously, and it introduces deep regularized clustering to reduce the unreliability of the similarity measures. In particular, we present a boundary-aware variational auto-encoder that tunes variable evidence lower bounds flexibly to assist feature representation learning better for more accurate clustering. Extensive experiments on various datasets from multiple domains demonstrate that the proposed method outperforms several popular comparison baseline methods.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Faculty/School / Institute/Centre: Current – Faculty of Health, Engineering and Sciences - School of Mathematics, Physics and Computing (1 Jan 2022 -)
Faculty/School / Institute/Centre: Current – Faculty of Health, Engineering and Sciences - School of Mathematics, Physics and Computing (1 Jan 2022 -)
Date Deposited: 17 Mar 2022 04:49
Last Modified: 22 Jun 2022 02:43
Uncontrolled Keywords: Unsupervised representation learning; Deep clustering; Variational bounds
Fields of Research (2008): 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining
08 Information and Computing Sciences > 0804 Data Format > 080403 Data Structures
08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080106 Image Processing
Fields of Research (2020): 46 INFORMATION AND COMPUTING SCIENCES > 4603 Computer vision and multimedia computation > 460306 Image processing
46 INFORMATION AND COMPUTING SCIENCES > 4603 Computer vision and multimedia computation > 460308 Pattern recognition
Socio-Economic Objectives (2008): E Expanding Knowledge > 97 Expanding Knowledge > 970108 Expanding Knowledge in the Information and Computing Sciences
Socio-Economic Objectives (2020): 28 EXPANDING KNOWLEDGE > 2801 Expanding knowledge > 280115 Expanding knowledge in the information and computing sciences
Identification Number or DOI: https://doi.org/10.1007/s11042-021-11597-2
URI: http://eprints.usq.edu.au/id/eprint/46998

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