Name
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. |
Year | Venue | Keywords |
|---|---|---|
2013 | neural information processing systems | image processing,clustering,algorithms |
DocType | Volume | Citations |
Conference | abs/1306.1185 | 21 |
PageRank | References | Authors |
0.77 | 9 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xavier Bresson | 1 | 1842 | 68.08 |
| Laurent, Thomas | 2 | 74 | 7.43 |
| Uminsky, David | 3 | 48 | 4.65 |
| James H. von Brecht | 4 | 93 | 6.45 |