Semi-Supervised Nonnegative Matrix Factorization
Nonnegative matrix factorization (NMF) is a popular method for low-rank approximation of nonnegative matrix, providing a useful tool for representation learning that is valuable for clustering and classification.
Yoo, J, Lee, H, Choi, S
core +1 more source
We propose the use of nonnegative matrix factorization (NMF) as a model-independent methodology to analyze neural activity. We demonstrate that, using this technique, it is possible to identify local spatiotemporal patterns of neural activity in the ...
Nicolelis Miguel AL +5 more
doaj +1 more source
Advances in independent component analysis and nonnegative matrix factorization [PDF]
A fundamental problem in machine learning research, as well as in many other disciplines, is finding a suitable representation of multivariate data, i.e. random vectors.
Yuan, Zhijian
core +1 more source
Sparse Separable Nonnegative Matrix Factorization
International audienceWe propose a new variant of nonnegative matrix factorization (NMF), combining separability and sparsity assumptions. Separability requires that the columns of the first NMF factor are equal to columns of the input matrix, while ...
Vandaele, Arnaud +3 more
core
Efficient hybrid algorithm for nonnegative matrix factorization based on modified nonmonotone linear search. [PDF]
Wu J, Li W, Su L, Wang H, Li Y.
europepmc +1 more source
Recovering missing features in nonnegative matrix factorization via generalized singular value decomposition. [PDF]
Guo Y, Holy TE.
europepmc +1 more source
Topology constrained nonnegative matrix factorization for time varying omic expression. [PDF]
Dey A +3 more
europepmc +1 more source
Non Negative (Kernel) Max-Margin Matrix Factorization [PDF]
Kumar B G, Vijay, Patras, Ioannis
core +4 more sources
Immune-related gene signature for predicting biochemical recurrence after RP in prostate cancer subtypes. [PDF]
Lin Y, Zhou J.
europepmc +1 more source
A Generalized NMF-Based Method for Analyzing Time-Resolved Spectroscopic Data. [PDF]
Kobeleva E, Chewle S, Horch M, Weber M.
europepmc +1 more source

