Incremental maximum margin clustering

被引：0
作者：
V. Vijaya Saradhi
P. Charly Abraham
机构：
[1] Indian Institute of Technology Guwahati,Department of Computer Science and Engineering
[2] Oracle Development Center,undefined
来源：
Pattern Analysis and Applications | 2016年 / 19卷
关键词：
Clustering; Large margin; Incremental clustering ; Support vector machines; Support vector regression;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
This paper proposes incremental maximum margin clustering in which one data point at a time is examined to decide which cluster the new data point belongs. The proposed method adopts the off-line iterative maximum margin clustering method’s alternating optimization algorithm. Accurate online support vector regression is employed in the alternating optimization. To avoid premature convergence, a sequence of decremental unlearning and incremental learning steps is performed. The proposed method is experimentally argued to (i) be scalable and competitive on training time front when compared with iterative maximum margin clustering and (ii) achieve competitive cluster quality compared to the off-line counterpart.
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页码：1057 / 1067
页数：10
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