Non-negative Matrix Factorization (NMF), a classical method for dimensionality reduction,
has been applied in many fields. It is based on the idea that negative numbers are physically …

In the real world, one object is usually described via multiple views or modalities. Many
existing multiview clustering methods fuse the information of multiple views by learning a …

As one of the most important information of the data, the geometry structure information is
usually modeled by a similarity graph to enforce the effectiveness of nonnegative matrix …

In this paper, we propose a new supervised non-negative matrix factorization algorithm,
named Robust Dual-graph Discriminative Non-negative Matrix Factorization (RDGDNMF) …

Nonnegative matrix factorization (NMF) is a very effective technique for image
representation, which has been widely applied in computer vision and pattern recognition …

Feature representation plays a critical role in speech emotion recognition (SER). As a
method of data dimensionality reduction, Non-negative Matrix Factorization (NMF) can …

Anomaly target detection has been a hotspot of the hyperspectral imagery (HSI) processing
in recent decades. One of the key research points in the HSI anomaly detection is the …

Nonnegative matrix factorization (NMF) is a powerful image representation algorithm in
pattern recognition and data mining. However, the traditional NMF does not utilize any label …

Many methods have been considered for gene selection and analysis of gene expression
data. Nonetheless, there still exists the considerable space for improving the explicitness …

In practical facial expression recognition (FER), the training data and test data are often
obtained from different domains. It is obvious that the domain disparity could significantly …