  Clustering on the data with multiple aspects, such as multi-view or
multi-type relational data, has become popular in recent years due to their
wide applicability. The approach using manifold learning with the Non-negative
Matrix Factorization (NMF) framework, that learns the accurate low-rank
representation of the multi-dimensional data, has shown effectiveness. We
propose to include the inter-manifold in the NMF framework, utilizing the
distance information of data points of different data types (or views) to learn
the diverse manifold for data clustering. Empirical analysis reveals that the
proposed method can find partial representations of various interrelated types
and select useful features during clustering. Results on several datasets
demonstrate that the proposed method outperforms the state-of-the-art
multi-aspect data clustering methods in both accuracy and efficiency.
