Kernel methods for identification faces

Author

Lai, Pei Ling

Author_Institution

Dept. of Comput. Sci., York Univ., UK

Volume

5

fYear

2002

fDate

18-22 Nov. 2002

Firstpage

2512

Abstract

We review a neural network implementation of the statistical technique of Principal Component Analysis (PCA) and Factor Analysis. We now derive a new method based on Kernel Principal Components Analysis (KPCA) and extend the Kernel PCA method to sparsified Kernel PCA. We then apply two methods to the data set which is composed of 10 faces in a mixture of poses. We wish to identify only the most significant poses on a data set. We found the better result from the sparsified Kernel PCA method.

Keywords

covariance matrices; eigenvalues and eigenfunctions; face recognition; neural nets; principal component analysis; unsupervised learning; covariance matrix; eigenvalues; eigenvectors; face identification; factor analysis; k-means algorithm; kernel principal components analysis; neural network implementation; principal component analysis; sparsified kernel PCA; unsupervised learning feature space; Computer science; Cost function; Covariance matrix; Eigenvalues and eigenfunctions; Humans; Kernel; Neural networks; Principal component analysis; Support vector machines; Unsupervised learning;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Information Processing, 2002. ICONIP '02. Proceedings of the 9th International Conference on

Print_ISBN

981-04-7524-1

Type

conf

DOI

10.1109/ICONIP.2002.1201947

Filename

1201947