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Journal of Information Science and Engineering, Vol. 31 No. 3, pp. 1149-1164 (May 2015)

Clustering Using Local and Global Exponential Discriminant Regularization*

Department of Computer and Information Sciences
Pakistan Institute of Engineering and Applied Sciences
Islamabad, 45650 Pakistan
E-mail: {nasir; jalil; asif}

In recently reported clustering approaches, both local and global information were utilized in order to effectively learn nonlinear manifold in image dataset. However, in each of these clustering approaches, regularization parameter had to be included to handle small-sample-size (SSS) problem of linear discriminant analysis (LDA). Due to which, we have to optimize a number of clustering parameters to report optimal clustering performance in these clustering models. In this study, we propose less-parameterized Local and Global Exponential Discriminant Regularization (LGEDR) clustering model. Our proposed LGEDR model is based on exponential discriminant analysis (EDA) in which SSS problem of LDA is handled without including regularization parameter. Because, no discriminant information of LDA is lost in EDA, clustering performance of the proposed LGEDR model is comparable over existing state-of-art clustering approaches on 12 benchmark image datasets. Further, due to less-parameterized nature, proposed LGEDR model is computationally efficient over existing clustering approaches that utilized both local and global information in image data.

Keywords: image clustering, manifold learning, local and global learning, exponential discriminant analysis, clustering models

Full Text () Retrieve PDF document (201505_21.pdf)

Received August 22, 2013; revised November 17, 2013 & March 3, 2014; accepted April 11, 2014.
Communicated by Chu-Song Chen.
* This work was supported by the HEC, Pakistan under Award No. 17-5-5(Ps5-217)/HEC/Sch/2010.