Co-spectral clustering based density peak
Yang Li, Weifeng Liu, Yanjiang Wang, Dapeng Tao · 2015
Spectral clustering employs spectral-graph structure of a similarity matrix to partition data into disjoint meaningful groups, because of its well-defined mathematical framework, good performance and simplicity, spectral clustering has gained considerable attentions in the recent past. Despite these virtues, it suffers from several drawbacks, such as it is unable to determine a reasonable cluster number, sensitive to initial condition and not robust to outliers. In this paper, we present a new approach named density peak spectral clustering (DPSC) which combines spectral clustering with density peak clustering algorithm (DPCA) into a unified framework to solve these problems. Since multi-view data is common in clustering problem, to further bootstrap the clustering performance by using complementary information from different view, then we propose co-trained density peak spectral clustering (Co-DPSC), which is an extension of DPSC to multi-views based on the co-training idea. Experimental comparisons with a number of baselines on a toy and three real-world datasets show the effectiveness of our proposed DPSC and Co-DPSC algorithm.