Learning Inter- and Intra-manifolds for Matrix Factorization-based Multi-Aspect Data Clustering
This work addresses clustering challenges for data with multiple aspects, offering an incremental improvement over existing NMF-based methods.
The paper tackles the problem of clustering multi-aspect data, such as multi-view or multi-type relational data, by incorporating inter-manifold learning into a Non-negative Matrix Factorization (NMF) framework, resulting in improved accuracy and efficiency compared to state-of-the-art methods.
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.