An equivalence between log-sum-exp approximation and entropy regularization in K-means clustering

被引：1

作者：

Inoue, Kohei ^{[1
]}

Hara, Kenji ^{[1
]}

机构：

[1] Kyushu Univ, Fac Design, Minami Ku, 4-9-1 Shiobaru, Fukuoka 8158540, Japan

来源：

IEICE NONLINEAR THEORY AND ITS APPLICATIONS | 2020年 / 11卷 / 04期

关键词：

K-means clustering; log-sum-exp approximation; entropy regularization; maximum entropy method;

D O I：

10.1587/nolta.11.446

中图分类号：

O1 [数学];

学科分类号：

0701 ; 070101 ;

摘要：

In this paper, we show an equivalence between log-sum-exp approximation and entropy regularization in K-means clustering, which is a well-known algorithm for partitional clustering. We derive an identical equation for updating centroids of clusters from the two formulations. Additionally, we derive an alternative equation suitable for another formulation of entropy regularization, maximum entropy method. We also show experimental results which support the theoretical results.

引用

页码：446 / 453

页数：8

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