Results 291 to 300 of about 141,420 (324)

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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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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Interpretable nonnegative matrix decompositions

Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, 2008
A matrix decomposition expresses a matrix as a product of at least two factor matrices. Equivalently, it expresses each column of the input matrix as a linear combination of the columns in the first factor matrix. The interpretability of the decompositions is a key issue in many data-analysis tasks. We propose two new matrix-decomposition problems: the
Saara Hyvönen, Pauli Miettinen, Evimaria Terzi   +2 more
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Manifold Peaks Nonnegative Matrix Factorization

IEEE Transactions on Neural Networks and Learning Systems
Nonnegative matrix factorization (NMF) has attracted increasing interest for its high interpretability in recent years. It is shown that the NMF is closely related to fuzzy k -means clustering, where the basis matrix represents the cluster centroids.
Xiaohua Xu, Ping He
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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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Nonnegative matrix factorization: When data is not nonnegative

2014 7th International Conference on Biomedical Engineering and Informatics, 2014
In this paper, we present a new variations of the popular nonnegative matrix factorization (NMF) approach to extend it to the data with negative values. When a NMF problem is formulated as μ ≈μμ, we try to develop a new method that only allows μ to contain nonnegative values, but allows both μ and μ to have both nonnegative and negative values. In this
Siyuan Wu, Jim Wang
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