Fast statistical learning with Kernel-based simple-fda

K. Nakaura, S. Karungaru, T. Akashi, Y. Mitsukura, M. Fukumi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, new statistical learning algorithms with kernel function are presented. Recently, iterative learning algorithms for obtaining eigenvectors in the principal component analysis (PCA) have been presented in the field of pattern recognition and neural network. However, the Fisher linear discriminant analysis (FLDA) has been used in many fields, especially face image analysis. The drawback of FLDA is a long computational time based on a large-sized covariance matrix and the issue that the within-class covariance matrix is usually singular. In order to overcome this difficulty, we proposed the feature generation method Simple-FLDA which is approximately derived from geometrical interpretation of FLDA. This algorithm is similar to Simple-PCA and does not need matrix operation. In this paper, new statistical kernel based learning algorithms are presented. They are extended versions of Simple-PCA and Simple-FLDA to nonlinear space using the kernel function. Their preliminary simulation results are given for a simple face recognition problem.

Original languageEnglish
Title of host publicationSITIS 2008 - Proceedings of the 4th International Conference on Signal Image Technology and Internet Based Systems
Pages333-337
Number of pages5
DOIs
Publication statusPublished - 2008 Dec 1
Externally publishedYes
Event4th International Conference on Signal Image Technology and Internet Based Systems, SITIS 2008 - Bali, Indonesia
Duration: 2008 Nov 302008 Dec 3

Publication series

NameSITIS 2008 - Proceedings of the 4th International Conference on Signal Image Technology and Internet Based Systems

Other

Other4th International Conference on Signal Image Technology and Internet Based Systems, SITIS 2008
CountryIndonesia
CityBali
Period08/11/3008/12/3

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ASJC Scopus subject areas

  • Computer Networks and Communications
  • Software

Cite this

Nakaura, K., Karungaru, S., Akashi, T., Mitsukura, Y., & Fukumi, M. (2008). Fast statistical learning with Kernel-based simple-fda. In SITIS 2008 - Proceedings of the 4th International Conference on Signal Image Technology and Internet Based Systems (pp. 333-337). [4725823] (SITIS 2008 - Proceedings of the 4th International Conference on Signal Image Technology and Internet Based Systems). https://doi.org/10.1109/SITIS.2008.52