Fast support vector clustering

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• 作者：Tung Pham ; Hang Dang ; Trung Le ; Thai Hoang Le
• 关键词：Support vector clustering ; Cluster analysis ; Kernel method
• 刊名：Vietnam Journal of Computer Science
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：4
• 期：1
• 页码：13-21
• 全文大小：872KB
• 刊物类别：Information Systems and Communication Service; Artificial Intelligence (incl. Robotics); Computer Ap
• 刊物主题：Information Systems and Communication Service; Artificial Intelligence (incl. Robotics); Computer Applications; e-Commerce/e-business; Computer Systems Organization and Communication Networks; Computa
• 出版者：Springer Berlin Heidelberg
• ISSN：2196-8896
• 卷排序：4

文摘

Support-based clustering has recently absorbed plenty of attention because of its applications in solving the difficult and diverse clustering or outlier detection problem. Support-based clustering method perambulates two phases: finding the domain of novelty and performing the clustering assignment. To find the domain of novelty, the training time given by the current solvers is typically over-quadratic in the training size. This fact impedes the application of support-based clustering method to the large-scale datasets. In this paper, we propose applying stochastic gradient descent framework to the first phase of support-based clustering for finding the domain of novelty in the form of a half-space and a new strategy to perform the clustering assignment. We validate our proposed method on several well-known datasets for clustering task to show that the proposed method renders a comparable clustering quality to the baselines while being faster than them.