On Dimensionality Reduction of Pattern Samples by Principal Components Analysis and Clustering

主成分分析法によるサンプル次元の減少とクラスターリングについて

On Dimensionality Reduction of Pattern Samples by Principal Components Analysis and Clustering

Creators Okada Toshihiko

Creators Tomita Shingo

Several methods for dimensionality reduction in the clustering procedures have been proposed. This paper reviews that a method of dimensionality reduction by principal components analysis is formed linear combinations of the features of given patterns. The main object of principal components analysis is to find a lower-dimensional representation that accounts the variance of the features. Moreover, the method of iterative improvement to minimize the sum-of-squared-error criterion for clustering is described. And to show the usefulness of the above mentioned method, the simulation for concrete pattern samples on a computer is excuted, and we can obtain the good agreement with our theoretical results.

[ISSN]0372-7661

[NCID]AN00244228