High-Dimensional Data Dimension Reduction Based on KECA

Yong De Hu; Jing Chang Pan; Xin Tan

doi:10.4028/www.scientific.net/AMM.303-306.1101

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 303-306High-Dimensional Data Dimension Reduction Based on...

High-Dimensional Data Dimension Reduction Based on KECA

Abstract:

Kernel entropy component analysis (KECA) reveals the original data’s structure by kernel matrix. This structure is related to the Renyi entropy of the data. KECA maintains the invariance of the original data’s structure by keeping the data’s Renyi entropy unchanged. This paper described the original data by several components on the purpose of dimension reduction. Then the KECA was applied in celestial spectra reduction and was compared with Principal Component Analysis (PCA) and Kernel Principal Component Analysis (KPCA) by experiments. Experimental results show that the KECA is a good method in high-dimensional data reduction.

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Periodical:

Applied Mechanics and Materials (Volumes 303-306)

Pages:

1101-1104

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.303-306.1101

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Online since:

February 2013

Authors:

Yong De Hu, Jing Chang Pan, Xin Tan

Keywords:

Dimension Reduction, KECA, KPCA, Principal Component Analysis (PCA), Support Vector Machine (SVM)

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