Transformed K-means Clustering

被引：0

作者：

Goel, Anurag ^{[1
]}

Majumdar, Angshul ^{[1
]}

机构：

[1] IIIT Delhi, New Delhi, India

来源：

29TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO 2021) | 2021年

关键词：

clustering; K-means; representation learning; FACTORIZATION;

D O I：

暂无

中图分类号：

O42 [声学];

学科分类号：

070206 ; 082403 ;

摘要：

In this work we propose a clustering framework based on the paradigm of transform learning. In simple terms the representation from transform learning is used for K-means clustering; however, the problem is not solved in such a naive piecemeal fashion. The K-means clustering loss is embedded into the transform learning framework and the joint problem is solved using the alternating direction method of multipliers. Results on document clustering show that our proposed approach improves over the state-of-the-art.

引用

页码：1526 / 1530

页数：5

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