Combining PCA and MCA by using recursive least square learning method

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review

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Detail(s)

Original languageEnglish
Title of host publication1998 IEEE International Conference on Electronics, Circuits and Systems
Subtitle of host publicationSurfing the Waves of Science and Technology
PublisherIEEE
Pages121-124
Volume2
ISBN (Print)0-7803-5008-1
Publication statusPublished - Sep 1998

Publication series

NameProceedings of the IEEE International Conference on Electronics, Circuits, and Systems

Conference

Title1998 5th IEEE International Conference on Electronics, Circuits and Systems (ICECS'98)
LocationInstituto Superior Técnico
PlacePortugal
CityLisboa
Period7 - 10 September 1998

Abstract

By using the fact that the derivatives of the ith network output with respect to the weights connected to the jth output neuron (ij) are zero, a modified RLS method is proposed for principal and minor components analysis. After the extraction of significant components of the input vectors, the error covariance matrix obtained in the learning process is used to perform minor components analysis. The minor components found are then pruned so as to achieve a higher compression ratio. Simulation results show that both the convergent speed and the compression ratio are improved. These indicate that our method combines the extraction of principal components and the pruning of minor components effectively.

Citation Format(s)

Combining PCA and MCA by using recursive least square learning method. / Wong, A. S. Y.; Wong, K. W.; Leung, C. S.

1998 IEEE International Conference on Electronics, Circuits and Systems: Surfing the Waves of Science and Technology. Vol. 2 IEEE, 1998. p. 121-124 (Proceedings of the IEEE International Conference on Electronics, Circuits, and Systems).

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review