Maximum Margin Clustering on Data Manifolds
Fei Wang, Xin Wang, Tao Li · 2009
Clustering is one of the most fundamental and important problems in computer vision and pattern recognition communities. Maximum margin clustering (MMC) is a recently proposed clustering technique which has shown promising experimental results. The main theme behind MMC is to extend the standard maximum margin principle in support vector machine (SVM) to the unsupervised scenario. This paper will consider the problem of maximum margin clustering on data manifolds. Specifically, we propose an approach called manifold regularized maximum margin clustering (MRMMC) which combines both the maximum margin data discrimination and data manifold information in a unified clustering objective and propose an efficient algorithm to solve it. Finally the experimental results on several real world data sets are presented to show the effectiveness of our method.