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Nonnegative Matrix Factorization [PDF]
, 2013 Matrix factorization or factor analysis is an important task that is helpful in the analysis of high-dimensional real-world data. SVD is a classical method for matrix factorization, which gives the optimal low-rank approximation to a real-valued matrix in terms of the squared error.
Ke-Lin Du, M. N. S. Swamy
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Complexity, 2018 We present a novel method, called graph sparse nonnegative matrix factorization, for dimensionality reduction. The affinity graph and sparse constraint are further taken into consideration in nonnegative matrix factorization and it is shown that the ...
Xiangguang Dai +2 more
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Tight Semi-nonnegative Matrix Factorization [PDF]
Pattern Recognition and Image Analysis, 2020 The nonnegative matrix factorization is a widely used, flexible matrix decomposition, finding applications in biology, image and signal processing and information retrieval, among other areas. Here we present a related matrix factorization. A multi-objective optimization problem finds conical combinations of templates that approximate a given data ...
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Complexity, 2018 Single-microphone speech enhancement algorithms by using nonnegative matrix factorization can only utilize the temporal and spectral diversity of the received signal, making the performance of the noise suppression degrade rapidly in a complex ...
Dong-xia Wang +4 more
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Computing a Nonnegative Matrix Factorization---Provably [PDF]
SIAM Journal on Computing, 2012 29 pages, 3 ...
Arora, Sanjeev +3 more
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Collaborative filtering based on nonnegative/binary matrix factorization
Frontiers in Big DataCollaborative filtering generates recommendations by exploiting user-item similarities based on rating data, which often contains numerous unrated items.
Yukino Terui +5 more
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Online Nonnegative Matrix Factorization With Outliers [PDF]
IEEE Transactions on Signal Processing, 2016 We propose a unified and systematic framework for performing online nonnegative matrix factorization in the presence of outliers. Our framework is particularly suited to large-scale data. We propose two solvers based on projected gradient descent and the alternating direction method of multipliers.
Zhao, Renbo, Tan, Vincent Y. F.
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Big Data Mining and AnalyticsNonnegative Matrix Factorization (NMF) is one of the most popular feature learning technologies in the field of machine learning and pattern recognition. It has been widely used and studied in the multi-view clustering tasks because of its effectiveness.
Guosheng Cui +3 more
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Journal of Statistical Theory and Applications (JSTA), 2020 The Probabilistic Latent Semantic Analysis has been related with the Singular Value Decomposition. Several problems occur when this comparative is done.
Pau Figuera Vinué +1 more
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Adaptive Graph Regularization Discriminant Nonnegative Matrix Factorization for Data Representation
IEEE Access, 2019 Nonnegative matrix factorization, as a classical part-based representation method, has been widely used in pattern recognition, data mining and other fields.
Lin Zhang +3 more
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