Xu, Mai, Dong, Haoyu, Chen, Chen, Li, Ling (2016) Unsupervised dictionary learning with Fisher discriminant for clustering. Neurocomputing, 194 . pp. 65-73. ISSN 0925-2312. (doi:10.1016/j.neucom.2016.01.076) (Access to this publication is currently restricted. You may be able to access a copy if URLs are provided) (KAR id:55515)
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Official URL: http://dx.doi.org/10.1016/j.neucom.2016.01.076 |
Abstract
In this paper, we propose a novel Fisher discriminant unsupervised dictionary learning (FD-UDL) approach, for improving the clustering performance of state-of-the-art dictionary learning approaches in unsupervised scenarios. This is achieved by employing a novel Fisher discriminant criterion on dictionary elements to encourage the diversity between different sub-dictionaries, and also the coherence within each sub-dictionary. Such a discriminant is incorporated to formulate the optimization problem of unsupervised dictionary learning. Furthermore, we provide an analytical solution to the proposed optimization problem, obtaining the learned dictionary for clustering tasks. Unlike previous approaches for unsupervised clustering, the proposed FD-UDL approach takes into account both within-class and between-class scatters of sub-dictionaries, rather than only considering diversity between different sub-dictionaries. Finally, experiments on synthetic data, face and handwritten digit clustering tasks show the improved clustering accuracy over other state-of-the-art dictionary learning and clustering approaches.
Item Type: | Article |
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DOI/Identification number: | 10.1016/j.neucom.2016.01.076 |
Uncontrolled keywords: | Fisher discriminant; Dictionary learning; Sparse representation; Unsupervised learning |
Subjects: |
Q Science > Q Science (General) Q Science > Q Science (General) > Q335 Artificial intelligence |
Divisions: | Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing |
Depositing User: | Caroline Li |
Date Deposited: | 18 May 2016 14:11 UTC |
Last Modified: | 17 Aug 2022 12:20 UTC |
Resource URI: | https://kar.kent.ac.uk/id/eprint/55515 (The current URI for this page, for reference purposes) |
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