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논문 기본 정보

자료유형
학술대회자료
저자정보
Suguru Yasutomi (Tokyo University of Agriculture and Technology) Toshihisa Tanaka (Tokyo University of Agriculture and Technology)
저널정보
대한전자공학회 ITC-CSCC :International Technical Conference on Circuits Systems, Computers and Communications ITC-CSCC 2015
발행연도
2015.6
수록면
38 - 41 (4page)

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초록· 키워드

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Convex clustering is a clustering method that does not require the number of clusters in advance. This method is based on the mixture models which have all the samples as the means of clusters and clustering is achieved by finding sparse mixing weights. However, a large number of iterations are needed until convergence because its objective function does not evaluate the sparsity of the mixing weights. This article derives an efficient algorithm for convex clustering with regularization that represents sparsity. Focusing on the fact that sparse solutions on the probability simplex which is the solution space of the convex clustering have a large ℓ₂-norm, we use the ℓ₂-norm to regularize the solution. Experimental results show that the proposed method converges faster ones and the proposed regularization is adequate to represent sparsity.

목차

Abstract
I. Introduction
II. Convex Clustering and Sparsity
III. Proposed Regularization based on ℓ₂-norm
IV. Experimental Results
V. Conclusions
References

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UCI(KEPA) : I410-ECN-0101-2016-569-001695446