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

Memoirs of the Faculty of Engineering, Yamaguchi University Volume 28 Issue 2 Page 241-248
published_at 1978
KJ00000156205.pdf
[fulltext] 505 KB
Title
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
Source Identifiers
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.
Subjects
電気電子工学 ( Other)
Languages jpn
Resource Type departmental bulletin paper
Publishers 山口大学工学部
Date Issued 1978
File Version Version of Record
Access Rights open access
Relations
[ISSN]0372-7661
[NCID]AN00244228
Schools 工学部