Accession Number:

ADA612811

Title:

Multiclass Total Variation Clustering

Descriptive Note:

Corporate Author:

CALIFORNIA UNIV LOS ANGELES DEPT OF MATHEMATICS

Report Date:

2014-12-01

Pagination or Media Count:

13.0

Abstract:

Ideas from the image processing literature have recently motivated a new set of clustering algorithms that rely on the concept of total variation. While these algorithms perform well for bi-partitioning tasks, their recursive extensions yield unimpressive results for multiclass clustering tasks. This paper presents a general framework for multiclass total variation clustering that does not rely on recursion. The results greatly outperform previous total variation algorithms and compare well with state-of-the-art NMF approaches.

Subject Categories:

  • Numerical Mathematics

Distribution Statement:

APPROVED FOR PUBLIC RELEASE