Minimum entropy, k-means, spectral clustering

被引：6
|
作者：
Lee, Y [1 ]
Choi, S [1 ]
机构：
[1] ETRI, Biometr Technol Res Team, Taejon 305350, South Korea
来源：
2004 IEEE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOLS 1-4, PROCEEDINGS | 2004年
关键词：
D O I：
10.1109/IJCNN.2004.1379882
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper addresses an information-theoretic aspect of k-means and spectral clustering. First, we revisit the k-means clustering and show that its objective function is approximately derived from the minimum entropy principle when the Renyi's quadratic entropy is used. Then we present " maximum within-clustering association that is derived using " quadratic distance measure in the framework of minimum entropy principle, which is very similar to a class of spectral clustering algorithms that is based on the eigen-decomposition method.
引用
收藏
页码：117 / 122
页数：6
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