A SUPERVISORY APPROACH TO SEMI-SUPERVISED CLUSTERING

被引：1

作者：

Conroy, Bryan ^{[1
]}

Xi, Yongxin Taylor ^{[1
]}

Ramadge, Peter ^{[1
]}

机构：

[1] Princeton Univ, Dept Elect Engn, Princeton, NJ 08544 USA

来源：

2010 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING | 2010年

关键词：

Clustering methods; Algorithms; Pattern classification; Learning systems;

D O I：

10.1109/ICASSP.2010.5495368

中图分类号：

O42 [声学];

学科分类号：

070206 ; 082403 ;

摘要：

We propose a new approach to semi-supervised clustering that utilizes boosting to simultaneously learn both a similarity measure and a clustering of the data from given instance-level must-link and cannot-link constraints. The approach is distinctive in that it uses a supervising feedback loop to gradually update the similarity while at the same time guiding an underlying unsupervised clustering algorithm. Our approach is grounded in the theory of boosting. We provide three examples of the clustering algorithm on real datasets.

引用

页码：1858 / 1861

页数：4

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