Memoirs of the Faculty of Engineering, Yamaguchi University

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Memoirs of the Faculty of Engineering, Yamaguchi University Volume 28 Issue 2
published_at 1978

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
Okada Toshihiko
Tomita Shingo
fulltext
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KJ00000156205.pdf
Descriptions
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.