Constrained low-rank matrix approximations have been known for decades as powerful
linear dimensionality reduction techniques able to extract the information contained in large …

Natural language processing (NLP) is useful for handling text and speech, and sequence
labeling plays an important role by automatically analyzing a sequence (text) to assign …

Although nonnegative matrix factorization (NMF) is widely used, some matrix factorization
methods result in misleading results and waste of computing resources due to lack of timely …

Community detection is a popular yet thorny issue in social network analysis. A symmetric
and nonnegative matrix factorization (SNMF) model based on a nonnegative multiplicative …

Abstract Deep Nonnegative Matrix Factorization (Deep NMF) is an effective strategy for
feature extraction in recent years. By decomposing the matrix recurrently on account of the …

In a real-world scenario, an object is easily considered as features combined by multiple
views in reality. Thus, multiview features can be encoded into a unified and discriminative …

Semi-supervised nonnegative matrix factorization exploits the strengths of matrix
factorization in successfully learning part-based representation and is also able to achieve …

Recent studies have shown the satisfactory results of the matrix factorization technique in
Multi-view Clustering (MVC). Compared with the single-layer formed clustering models, the …

Deep matrix factorization (DMF) has been demon-strated to be a powerful tool to take in the
complex hierarchical information of multi-view data (MDR). However, existing multi-view …

Most of existing nonnegative matrix factorization (NMF) methods do not fully exploit global
and local similarity information from data. In this paper, we propose a novel local similarity …