An Effective Method for Classification of High Dimensional Data

Author

Lam, Benson S Y ; Yan, Hong

Author_Institution

City Univ. of Hong Kong, Hong Kong

Volume

5

fYear

2007

fDate

19-22 Aug. 2007

Firstpage

2713

Lastpage

2718

Abstract

We study a new high dimensional data problem in this paper. In pattern classification, if many dimensions of two groups share a similar distribution, the classification error rates will be 50%. We have proposed a new clustering algorithm to deal with this problem. Its basic idea is to confine the support of the optimization equation so that the data points in one group can only have small contribution to the estimated cluster center in another group. Experiments show that the proposed method is able to yield good results in eight real world data sets and its performance is better than 10 existing methods.

Keywords

learning (artificial intelligence); pattern classification; pattern clustering; classification error rates; clustering algorithm; high dimensional data; machine learning; optimization equation; pattern classification; Clustering algorithms; Cybernetics; Data engineering; Electronic mail; Equations; Error analysis; Handwriting recognition; Machine learning; Pattern classification; Shape; Calculus of variations; Classifcation of high imensional data; Clustering; Machine learning;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Cybernetics, 2007 International Conference on

Conference_Location

Hong Kong

Print_ISBN

978-1-4244-0973-0

Electronic_ISBN

978-1-4244-0973-0

Type

conf

DOI

10.1109/ICMLC.2007.4370608

Filename

4370608