Results 271 to 280 of about 32,711 (308)

Unilateral Orthogonal Nonnegative Matrix Factorization

SIAM Journal on Control and Optimization, 2023
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Shang, Jun, Chen, Tongwen
openaire   +2 more sources

Quadratic nonnegative matrix factorization

Pattern Recognition, 2012
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Yang, Zhirong, Oja, Erkki
openaire   +5 more sources

Factor-Bounded Nonnegative Matrix Factorization

ACM Transactions on Knowledge Discovery from Data, 2021
Nonnegative Matrix Factorization (NMF) is broadly used to determine class membership in a variety of clustering applications. From movie recommendations and image clustering to visual feature extractions, NMF has applications to solve a large number of knowledge discovery and data mining problems.
Kai Liu   +4 more
openaire   +1 more source

Nonnegative Unimodal Matrix Factorization

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2021
We introduce a new Nonnegative Matrix Factorization (NMF) model called Nonnegative Unimodal Matrix Factorization (NuMF), which adds on top of NMF the unimodal condition on the columns of the basis matrix. NuMF finds applications for example in analytical chemistry.
Andersen Man Shun Ang   +3 more
openaire   +1 more source

Nonsmooth nonnegative matrix factorization (nsNMF)

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006
We propose a novel nonnegative matrix factorization model that aims at finding localized, part-based, representations of nonnegative multivariate data items. Unlike the classical nonnegative matrix factorization (NMF) technique, this new model, denoted "nonsmooth nonnegative matrix factorization" (nsNMF), corresponds to the optimization of an ...
Alberto, Pascual-Montano   +4 more
openaire   +2 more sources

Nonnegative Matrix Factorization

Proceedings of the 2015 ACM International Symposium on Symbolic and Algebraic Computation, 2015
How quickly can we compute the nonnegative rank (r) of an m x n matrix? This problem ---- and the companion problem of finding a nonnegative matrix factorization with minimum inner-dimension ---- has a rich history, with applications in quantum mechanics, probability theory, data analysis, communication complexity and polyhedral combinatorics.
openaire   +1 more source

Large-Cone Nonnegative Matrix Factorization

IEEE Transactions on Neural Networks and Learning Systems, 2016
Nonnegative matrix factorization (NMF) has been greatly popularized by its parts-based interpretation and the effective multiplicative updating rule for searching local solutions. In this paper, we study the problem of how to obtain an attractive local solution for NMF, which not only fits the given training data well but also generalizes well on the ...
Tongliang, Liu   +2 more
openaire   +2 more sources

Labelwalking nonnegative matrix factorization

2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2015
Semi-supervised learning (SSL) utilizes plenty of unlabeled examples to boost the performance of learning from limited labeled examples. Due to its great discriminant power, SSL has been widely applied to various real-world tasks such as information retrieval, pattern recognition, and speech separa- tion.
Long Lan   +4 more
openaire   +1 more source

Weighted nonnegative matrix factorization

2009 IEEE International Conference on Acoustics, Speech and Signal Processing, 2009
Nonnegative matrix factorization (NMF) is a widely-used method for low-rank approximation (LRA) of a nonnegative matrix (matrix with only nonnegative entries), where nonnegativity constraints are imposed on factor matrices in the decomposition. A large body of past work on NMF has focused on the case where the data matrix is complete.
Yong-Deok Kim, Seungjin Choi
openaire   +1 more source

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