Please use this identifier to cite or link to this item:
https://hdl.handle.net/1959.11/5632
Title: | Comprehensive Analysis for the Local Fisher Discriminant Analysis | Contributor(s): | Gao, Junbin (author); Kwan, Paul H (author); Huang, Xiaodi (author) | Publication Date: | 2009 | DOI: | 10.1142/S0218001409007478 | Handle Link: | https://hdl.handle.net/1959.11/5632 | Abstract: | Using local data information, the recently proposed local Fisher Discriminant Analysis (LFDA) algorithm provides a new way of handling the multimodal issues within classes where the conventional Fisher Discriminant Analysis (FDA) algorithm fails. Like the FDA algorithm (global counterpart), the LFDA suffers when it is applied to the higher dimensional data sets. In this paper, we propose a new formulation by which a robust algorithm can be formed. The new algorithm offers more robust results for higher dimensional data sets when compared with the LFDA in most cases. By extensive simulation studies, we have demonstrated the practical usefulness and robustness of our new algorithm in data visualization. | Publication Type: | Journal Article | Source of Publication: | International Journal of Pattern Recognition and Artificial Intelligence, 23(6), p. 1129-1143 | Publisher: | World Scientific Publishing Company | Place of Publication: | Singapore | ISSN: | 1793-6381 0218-0014 |
Fields of Research (FoR) 2008: | 080108 Neural, Evolutionary and Fuzzy Computation 080109 Pattern Recognition and Data Mining |
Socio-Economic Objective (SEO) 2008: | 890202 Application Tools and System Utilities | Peer Reviewed: | Yes | HERDC Category Description: | C1 Refereed Article in a Scholarly Journal |
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Appears in Collections: | Journal Article |
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