ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development ›› 2019, Vol. 56 ›› Issue (8): 1677-1685.doi: 10.7544/issn1000-1239.2019.20190150

Special Issue: 2019人工智能前沿进展专题

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Sample-Weighted Multi-View Clustering

Hong Min1,2, Jia Caiyan1,2, Li Yafang3, Yu Jian1,2   

  1. 1(Beijing Key Laboratory of Traffic Data Analysis and Mining (Beijing Jiaotong University), Beijing 100044);2(School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044);3(Faculty of Information Technology, Beijing University of Technology, Beijing 100124)
  • Online:2019-08-01

Abstract: In the era of big data, the ability of humans to collect, store, transmit and manage data has been increasingly improved. Various industries have accumulated a large amount of data resources, which are often multi-source and heterogeneous. How to effectively cluster these multi-source data (also known as multi-view clustering) has become one of the focuses of today’s machine learning research. The existing multi-view clustering algorithms mainly pay attention to the contribution of different views and features to the cluster structure from the “global” perspective, without considering the “local” information complementary differences between different samples. Therefore, this paper proposes a new sample-weighted multi-view clustering (SWMVC). The method weights each sample with different views and adopts alternating direction method of multipliers (ADMM) to learn sample weight, which can not only learn the “local” difference of weights among multiple views in different sample points, but also reflect the “global” difference of the contribution of different views to the cluster structure, and has better flexibility. Experiments on multiple datasets show that the SWMVC method has a better clustering effect on heterogeneous view data.

Key words: data mining, multi-view, cluster, K-means, sample weights

CLC Number: