Title :
A Unified Framework for Dimensionality Reduction
Author :
Ma, Fei ; Chen, Jie
Author_Institution :
Sch. of Math. & Comput. Sci., Sun Yat-sen Univ., Guangzhou, China
Abstract :
In this paper, we construct a unified framework for dimensionality reduction, for simplicity we call it essential kernel principal component analysis (EKPCA). Some of well-known dimensionality reduction methods, such as kernel principal component analysis, locally linear embedding, Laplacian eigenmaps, Isomaps, diffusion maps are subject to this framework.
Keywords :
eigenvalues and eigenfunctions; principal component analysis; Isomaps; Laplacian eigenmaps; diffusion maps; dimensionality reduction; essential kernel principal component analysis; locally linear embedding; Data mining; Eigenvalues and eigenfunctions; Feature extraction; Hilbert space; Kernel; Principal component analysis;
Conference_Titel :
Pattern Recognition (CCPR), 2010 Chinese Conference on
Conference_Location :
Chongqing
Print_ISBN :
978-1-4244-7209-3
Electronic_ISBN :
978-1-4244-7210-9
DOI :
10.1109/CCPR.2010.5659134
