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Nonnegative Low Rank Matrix Approximation for Nonnegative Matrices [PDF]
Applied Mathematics Letters, 2020 25 pages 13 ...
Michael Ng, Guang-Jing Song
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Nonnegative matrix factorization: When data is not nonnegative
2014 7th International Conference on Biomedical Engineering and Informatics, 2014In 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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Quadratic nonnegative matrix factorization
Pattern Recognition, 2012zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Yang, Zhirong, Oja, Erkki
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Elastic Nonnegative Matrix Factorization
2018 IEEE International Conference on Data Mining Workshops (ICDMW), 2018Nonnegative 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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Labelwalking nonnegative matrix factorization
2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2015Semi-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
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Nonnegative Discriminant Matrix Factorization
IEEE Transactions on Circuits and Systems for Video Technology, 2017Nonnegative 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 +5 more
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Nonsmooth Nonnegative Matrix Factorization (nsNMF)
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006We 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 ...
Roberto D Pascual-Marqui +2 more
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Nonnegative Matrix Factorization With Regularizations
IEEE Journal on Emerging and Selected Topics in Circuits and Systems, 2014Matrix factorization techniques have been frequently applied in many fields. Among them, nonnegative matrix factorization (NMF) has received considerable attention for it aims to find a parts-based, linear representations of nonnegative data. Recently, many researchers propose various manifold learning algorithms to enhance learning performance by ...
Weiya Ren, Guohui Li, Dan Tu, Li Jia
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Weighted nonnegative matrix factorization
2009 IEEE International Conference on Acoustics, Speech and Signal Processing, 2009Nonnegative 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, 2008A 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 +2 more
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