Unsupervised dictionary learning with Fisher discriminant for clustering

Xu, Mai and Dong, Haoyu and Chen, Chen and Li, Ling (2016) Unsupervised dictionary learning with Fisher discriminant for clustering. Neurocomputing, 194 . pp. 65-73. ISSN 0925-2312. (doi:https://doi.org/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)

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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
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: Faculties > Sciences > School of Computing > Computational Intelligence Group
Faculties > Sciences > School of Computing > Data Science
Depositing User: Caroline Li
Date Deposited: 18 May 2016 14:11 UTC
Last Modified: 29 Jul 2016 08:39 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/55515 (The current URI for this page, for reference purposes)
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