Abstract
Subspace clustering splits data instances that are drawn from special low-dimensional subspaces via utilizing similarities between them. Traditional methods contain two steps: (1) learning the affinity matrix and (2) clustering on the affinity matrix. Although these two steps can alternatively contribute to each other, there exist heavy dependencies between the performance and the initial quality of affinity matrix. In this paper, we propose an efficient direct structured subspace clustering approach to reduce the quality effects of the affinity matrix on performances. We first analyze the connection between the affinity and partition matrices, and then fuse the computation of affinity and partition matrices. This fusion allows better preserving the subspace structures which help strengthen connections between data points in the same subspaces. In addition, we introduce an algorithm to optimize our proposed method. We conduct comparative experiments on multiple data sets with state-of-the-art methods. Our method achieves better or comparable performances.
Original language | English |
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Title of host publication | Neural Information Processing |
Subtitle of host publication | Proceedings, Part IV |
Editors | Long Cheng, Andrew Chi Sing Leung, Seiichi Ozawa |
Publisher | Springer Nature Switzerland AG |
Pages | 181-190 |
ISBN (Electronic) | 9783030042127 |
ISBN (Print) | 9783030042110 |
DOIs | |
Publication status | Published - Dec 2018 |
Event | 25th International Conference on Neural Information Processing (ICONIP 2018) - Sokha Siem Reap Resort & Convention Center, Siem Reap, Cambodia Duration: 13 Dec 2018 → 16 Dec 2018 https://conference.cs.cityu.edu.hk/iconip/ |
Publication series
Name | Lecture Notes in Computer Science |
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Volume | LNCS 11304 |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | 25th International Conference on Neural Information Processing (ICONIP 2018) |
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Abbreviated title | ICONIP 2018 |
Country/Territory | Cambodia |
City | Siem Reap |
Period | 13/12/18 → 16/12/18 |
Internet address |
Research Keywords
- Subspace clustering
- Unsupervised learning