Non-negative matrix factorization with locality constrained adaptive graph
Non-negative matrix factorization (NMF) has recently attracted much attention due to its
good interpretation in perception science and widely applications in various fields. In this …
good interpretation in perception science and widely applications in various fields. In this …
Large-cone nonnegative matrix factorization
Nonnegative matrix factorization (NMF) has been greatly popularized by its parts-based
interpretation and the effective multiplicative updating rule for searching local solutions. In …
interpretation and the effective multiplicative updating rule for searching local solutions. In …
Graph regularized discriminative non-negative matrix factorization for face recognition
Non-negative matrix factorization (NMF) has been widely employed in computer vision and
pattern recognition fields since the learned bases can be interpreted as a natural parts …
pattern recognition fields since the learned bases can be interpreted as a natural parts …
FastNMF: highly efficient monotonic fixed-point nonnegative matrix factorization algorithm with good applicability
L Li, YJ Zhang - Journal of Electronic Imaging, 2009 - spiedigitallibrary.org
Nonnegative matrix factorization (NMF) is a recently developed method for dimensionality
reduction, feature extraction, and data mining, etc. Currently, no NMF algorithm holds both …
reduction, feature extraction, and data mining, etc. Currently, no NMF algorithm holds both …
Feature weighted non-negative matrix factorization
Non-negative matrix factorization (NMF) is one of the most popular techniques for data
representation and clustering and has been widely used in machine learning and data …
representation and clustering and has been widely used in machine learning and data …
Clustering-based initialization for non-negative matrix factorization
Y Xue, CS Tong, Y Chen, WS Chen - Applied Mathematics and …, 2008 - Elsevier
Non-negative matrix factorization (NMF) is an unsupervised learning algorithm that can
extract parts from visual data. The goal of this technique is to find intuitive basis such that …
extract parts from visual data. The goal of this technique is to find intuitive basis such that …
[HTML][HTML] Feature selection and multi-kernel learning for adaptive graph regularized nonnegative matrix factorization
Nonnegative matrix factorization (NMF), a popular part-based representation technique,
does not capture the intrinsic local geometric structure of the data space. Graph regularized …
does not capture the intrinsic local geometric structure of the data space. Graph regularized …
Graph regularized nonnegative matrix factorization for data representation
Matrix factorization techniques have been frequently applied in information retrieval,
computer vision, and pattern recognition. Among them, Nonnegative Matrix Factorization …
computer vision, and pattern recognition. Among them, Nonnegative Matrix Factorization …
Adaptive graph regularized nonnegative matrix factorization for data representation
L Zhang, Z Liu, J Pu, B Song - Applied Intelligence, 2020 - Springer
As a classical data representation method, nonnegative matrix factorization (NMF) can well
capture the global structure information of the observed data, and it has been successfully …
capture the global structure information of the observed data, and it has been successfully …
Manifold regularized discriminative nonnegative matrix factorization with fast gradient descent
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 …
and has been widely used in image processing and pattern-recognition problems. This is …