Multi-Source Clustering based on spectral recovery

Hongwei Yin, Fanzhang Li, Li Zhang

Abstract: The research and analysis on multi-source data is one of important tasks in information science. Compared with traditional single-source data learning algorithms, multi-source data learning ones can describe objects more real and complete. Meanwhile, the learning process of multi-source data is more in line with the cognitive mechanism of human brain. So far, the research on multi-source data learning algorithms includes three classes, multi-source data transfer learning, multi-source data collaborative learning and multi-source multi-view learning. The traditional multi-source multi-view learning algorithms lack the ability of handling with the data missing issue, which means that these algorithms require the multi-source data to be complete. This paper proposes a multi-source clustering algorithm. Based on the spectral properties of Laplace operator, we first obtain the complete representation of multi-source data. Then, we utilize the multi-view spectral embedding (MVSE) to construct the fusion model. Experimental results show that our proposed method can improve the ability of clustering efficiently in the case of data missing.