Face recognition technology is a biometric technology, which is based on the identification
of facial features of a person. People collect the face images, and the recognition equipment …

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

Nonnegative matrix factorization (NMF) has become a widely used tool for the analysis of
high-dimensional data as it automatically extracts sparse and meaningful features from a set …

Identifying the underlying structure of a data set and extracting meaningful information is a
key problem in data analysis. Simple and powerful methods to achieve this goal are linear …

Nonnegative matrix factorization (NMF) is a standard linear dimensionality reduction
technique for nonnegative data sets. In order to measure the discrepancy between the input …

Nonnegative matrix factorization (NMF) is a popular approach to extract intrinsic features
from the original data. As the nonconvexity of NMF formulation, it always leads to degrade …

Non-negative matrix factorization (NMF) is a powerful tool for data science researchers, and
it has been successfully applied to data mining and machine learning community, due to its …

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 …

Parts-based representations, such as non-negative matrix factorization and topic modeling,
have been used to identify structure from single-cell sequencing data sets, in particular …

We propose inertial versions of block coordinate descent methods for solving non-convex
non-smooth composite optimization problems. Our methods possess three main advantages …