Clustering and finding the number of clusters by unsupervised learning of mixture models using vector quantization

被引：0

作者：

Yoon, Sangho ^{[1
]}

Gray, Robert M. ^{[1
]}

机构：

[1] Stanford Univ, Informat Syst Lab, Elect Engn, Stanford, CA 94305 USA

来源：

2007 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, VOL III, PTS 1-3, PROCEEDINGS | 2007年

基金：

美国国家科学基金会;

关键词：

clustering; vector quantization; mixture models;

D O I：

暂无

中图分类号：

O42 [声学];

学科分类号：

070206 ; 082403 ;

摘要：

A new Lagrangian formulation with entropy and codebook size was proposed to extend the Lagrangian formulation of variable-rate vector quantization. We use the new Lagrangian formulation to perform clustering and to find the number of clusters by fitting mixture models to data using vector quantization. Experimental results show that the entropy and memory constrained vector quantization outperforms the state-of-the art model selection algorithms in the examples considered.

引用

页码：1081 / +

页数：2

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