.png){kind=icon}
.png){kind=icon}
Return
Fast support-based clustering method for large-scale problems
.png){kind=icon}
PRE
AI
DOI：10.1016/j.patcog.2009.12.010.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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. (C) 2009 Elsevier Ltd. All rights reserved
Keywords:
Large-scale problem
Kernel methods
Support vector clustering
Cluster labeling
Dynamical system
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.