Results 31 to 40 of about 4,116 (162)
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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Heuristics for exact nonnegative matrix factorization [PDF]
Journal of Global Optimization, 2015 32 pages, 2 figures, 16 ...
Arnaud Vandaele +3 more
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Simplicial nonnegative matrix factorization
The 2013 RIVF International Conference on Computing & Communication Technologies - Research, Innovation, and Vision for Future (RIVF), 2013 Nonnegative matrix factorization (NMF) plays a crucial role in machine learning and data mining, especially for dimension reduction and component analysis. It is employed widely in different fields such as information retrieval, image processing, etc. After a decade of fast development, severe limitations still remained in NMFs methods including high ...
Duy Khuong Nguyen, Khoat Than, Tu Bao Ho
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Monotonous (semi-)nonnegative matrix factorization [PDF]
Proceedings of the Second ACM IKDD Conference on Data Sciences, 2015 Nonnegative matrix factorization (NMF) factorizes a non-negative matrix into product of two non-negative matrices, namely a signal matrix and a mixing matrix. NMF suffers from the scale and ordering ambiguities. Often, the source signals can be monotonous in nature.
Nirav Bhatt, Arun Ayyar
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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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Nonnegative Matrix Factorization with Transform Learning [PDF]
2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018 Traditional NMF-based signal decomposition relies on the factorization of spectral data, which is typically computed by means of short-time frequency transform. In this paper we propose to relax the choice of a pre-fixed transform and learn a short-time orthogonal transform together with the factorization.
Dylan Fagot +2 more
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Partial Identifiability for Nonnegative Matrix Factorization
SIAM Journal on Matrix Analysis and Applications, 2023 27 pages, 8 figures, 7 examples. This third version makes minor modifications. Paper accepted in SIAM J.
Nicolas Gillis, Róbert Rajkó
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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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Online kernel nonnegative matrix factorization [PDF]
Signal Processing, 2017 Nonnegative matrix factorization (NMF) has become a prominent signal processing and data analysis technique. To address streaming data, online methods for NMF have been introduced recently, mainly restricted to the linear model. In this paper, we propose a framework for online nonlinear NMF, where the factorization is conducted in a kernel-induced ...
Zhu, Fei, Honeine, Paul
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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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