NDLI: RPCL-based local PCA algorithm

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2002 IEEE International Conference on Data Mining, 2002. Proceedings.

Proceedings 2001 IEEE International Conference on Data Mining

Proceedings 2001 IEEE International Conference on Data Mining

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RPCL-based local PCA algorithm

Content Provider	IEEE Xplore Digital Library
Author	Zhiyong Liu Lei Xu
Copyright Year	2001
Description	Author affiliation: Dept. of Comput. Sci. & Eng., Chinese Univ. of Hong Kong, China (Zhiyong Liu)
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.
Sponsorship	IEEE Comput. Soc. Tech. Committe on Pattern Anal. & Machine Intelligence (TCPAMI)
Starting Page	621
Ending Page	622
File Size	193936
Page Count	2
File Format	PDF
ISBN	0769511198
DOI	10.1109/ICDM.2001.989582
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2001-11-29
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Principal component analysis Covariance matrix Clustering algorithms Shape Computer science Data analysis Costs
Content Type	Text
Resource Type	Article

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