Nonnegative Matrix Factorization (NMF), a relatively novel paradigm for dimensionality
reduction, has been in the ascendant since its inception. It incorporates the nonnegativity …

Recently, we have witnessed an explosive growth in both the quantity and dimension of data
generated, which aggravates the high dimensionality challenge in tasks such as predictive …

Semi-Non-negative Matrix Factorization is a technique that learns a low-dimensional
representation of a dataset that lends itself to a clustering interpretation. It is possible that the …

Matrix factorization techniques have been frequently applied in information retrieval,
computer vision, and pattern recognition. Among them, Nonnegative Matrix Factorization …

Dimensionality reduction has attracted increasing attention, because high-dimensional data
have arisen naturally in numerous domains in recent years. As one popular dimensionality …

Non-negative matrix factorization (NMF) has been one of the most popular methods for
feature learning in the field of machine learning and computer vision. Most existing works …

Nonnegative matrix factorization (NMF) has become a popular dimension-reduction method
and has been widely applied to image processing and pattern recognition problems …

Nonnegative matrix factorization (NMF) has become a popular data-representation method
and has been widely used in image processing and pattern-recognition problems. This is …

In this paper, a novel graph-preserving sparse nonnegative matrix factorization (GSNMF)
algorithm is proposed for facial expression recognition. The GSNMF algorithm is derived …

Semi-NMF is a matrix factorization technique that learns a low-dimensional representation of
a dataset that lends itself to a clustering interpretation. It is possible that the map** …