Name
Abstract | ||
|---|---|---|
In many support vector-based clustering algorithms, a key computational bottleneck is the cluster labeling time of each data point which restricts the scalability of the method. In this paper, we review a general framework of support vector-based clustering using dynamical system and propose a novel method to speed up labeling time which is log-linear to the size of data. We also give theoretical background of the proposed method. Various large-scale benchmark results are provided to show the effectiveness and efficiency of the proposed method. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1016/j.patcog.2009.12.010 | Pattern Recognition |
Keywords | DocType | Volume |
key computational bottleneck,dynamical system,support vector clustering,data point,general framework,kernel methods,large-scale problem,cluster labeling,novel method,various large-scale benchmark result,theoretical background,clustering method,dynamic system,support vector,kernel method | Journal | 43 |
Issue | ISSN | Citations |
5 | Pattern Recognition | 27 |
PageRank | References | Authors |
0.91 | 15 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kyu-Hwan Jung | 1 | 82 | 4.82 |
| Daewon Lee | 2 | 989 | 58.67 |
| Jaewook Lee | 3 | 735 | 50.24 |