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Clustering on the data with multiple aspects, such as multi-view ormulti-type relational data, has become popular in recent years due to theirwide applicability. The approach using manifold learning with the Non-negativeMatrix Factorization (NMF) framework, that learns the accurate low-rankrepresentation of the multi-dimensional data, has shown effectiveness. Wepropose to include the inter-manifold in the NMF framework, utilizing thedistance information of data points of different data types (or views) to learnthe diverse manifold for data clustering. Empirical analysis reveals that theproposed method can find partial representations of various interrelated typesand select useful features during clustering. Results on several datasetsdemonstrate that the proposed method outperforms the state-of-the-artmulti-aspect data clustering methods in both accuracy and efficiency.

Learning Inter- and Intra-manifolds for Matrix Factorization-based Multi-Aspect Data Clustering