MMSVC: An Efficient Unsupervised Learning Approach for Large-Scale Datasets
This paper presents a multi-scale, hierarchical framework to extend the scalability of support vector clustering (SVC). Based on the multi-sphere support vector clustering, the clustering algorithm called multi-scale multi-sphere support vector clustering (MMSVC) in this framework works in a coarse-to-fine and top-to-down manner. Given one parent cluster, the next learning scale is generated by a secant-like numerical algorithm. A local quantity called spherical support vector density (sSVD) is proposed as a cluster validity measure which describes the compactness of the cluster. It is used as a terminate term in our framework. When dealing with large-scale dataset, our method benefits from the online learning, easy parameters tuning and the learning efficiency. 1.5 million tiny images were used to evaluate the method. Experimental results demonstrate that the method greatly improves the scalability and learning efficiency of support vector clustering.
one-class support vector machine large-scale clustering support vector clustering
Hong Gu Guangzhou Zhao Jianliang Zhang
College of Electric Engineering, Zhejiang University, Hangzhou, China, 310027
国际会议
无锡
英文
1-9
2010-09-17（万方平台首次上网日期，不代表论文的发表时间）