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Conference Proceeding

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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.


Presented at: 27th Annual Conference on Neural Information Processing Systems, NIPS 2013; Lake Tahoe, NV; United States; 5 December 2013 through 10 December 2013

Published by MIT Press

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Mathematics Commons