RPCL-based local PCA algorithm

Author

Liu, Zhiyong ; Xu, Lei

Author_Institution

Dept. of Comput. Sci. & Eng., Chinese Univ. of Hong Kong, China

fYear

2001

fDate

2001

Firstpage

621

Lastpage

622

Abstract

Mining local structure is important in data analysis. Gaussian mixture is able to describe local structure through covariance matrices, but when used on high-dimensional data, specifying such a large number of d(d+1)/2 free elements in each covariance matrix is difficult. By constraining the covariance matrix in decomposed orthonormal form, we propose a Local PCA algorithm to tackle this problem with the help of RPCL (Rival Penalized Competitive Learning), which can automatically determine the number of local structures

Keywords

Gaussian processes; covariance matrices; data mining; unsupervised learning; Gaussian mixture; RPCL competitive learning; RPCL-based Local PCA Algorithm; Rival Penalized Competitive Learning; covariance matrices; data analysis; decomposed orthonormal form; high dimensional data; local structure; local structure mining; local structures; Clustering algorithms; Computer science; Costs; Covariance matrix; Data analysis; Principal component analysis; Shape;

fLanguage

English

Publisher

ieee

Conference_Titel

Data Mining, 2001. ICDM 2001, Proceedings IEEE International Conference on

Conference_Location

San Jose, CA

Print_ISBN

0-7695-1119-8

Type

conf

DOI

10.1109/ICDM.2001.989582

Filename

989582

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2335789