Results 211 to 220 of about 4,031 (246)

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, Xiangyu Li, Zhihui Zhu, Lodewijk Brand, Hua Wang   +4 more
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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, Nicolas Gillis, Arnaud Vandaele, Hans De Sterck   +3 more
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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, J M, Carazo, Kieko, Kochi, Dietrich, Lehmann, Roberto D, Pascual-Marqui   +4 more
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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.
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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, Mingming, Gong, Dacheng, Tao   +2 more
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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, Naiyang Guan, Xiang Zhang, Xuhui Huang, Zhigang Luo   +4 more
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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
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Nonnegative Discriminant Matrix Factorization

IEEE Transactions on Circuits and Systems for Video Technology, 2017
Nonnegative matrix factorization (NMF), which aims at obtaining the nonnegative low-dimensional representation of data, has received wide attention. To obtain more effective nonnegative discriminant bases from the original NMF, in this paper, a novel method called nonnegative discriminant matrix factorization (NDMF) is proposed for image classification.
Yuwu Lu, Zhihui Lai, Yong Xu, Xuelong Li, David Zhang, Chun Yuan   +5 more
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Elastic Nonnegative Matrix Factorization

2018 IEEE International Conference on Data Mining Workshops (ICDMW), 2018
Nonnegative Matrix Factorization factors a large matrix into smaller nonnegative components. Non-negative models are often more amenable to interpretation vis-a-vis standard principal component analysis where negative entries may not correspond to any physical process.
Peter Ballen, Sudipto Guha
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Elastic nonnegative matrix factorization

Pattern Recognition, 2019
Abstract Nonnegative matrix factorization (NMF) plays a vital role in data mining and machine learning fields. Standard NMF utilizes the Frobenius norm while robust NMF uses the robust l2,1-norm to measure the quality of factorization, given the assumption of i.i.d Gaussian noise model and i.i.d Laplacian noise model, respectively.
He Xiong, Deguang Kong
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