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Unsupervised dictionary learning with Fisher discriminant for clustering

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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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
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: (The current URI for this page, for reference purposes)
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