Results 31 to 40 of about 7,807 (178)
A Symmetric Rank-one Quasi Newton Method for Non-negative Matrix Factorization [PDF]
, 2013 As we all known, the nonnegative matrix factorization (NMF) is a dimension reduction method that has been widely used in image processing, text compressing and signal processing etc.
Lai, Shu-Zhen +2 more
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Hyperspectral Unmixing Based on Nonnegative Matrix Factorization: A Comprehensive Review
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022 Hyperspectral unmixing has been an important technique that estimates a set of endmembers and their corresponding abundances from a hyperspectral image (HSI).
Xin-Ru Feng +5 more
doaj +1 more source
Robustness Analysis of Hottopixx, a Linear Programming Model for Factoring Nonnegative Matrices [PDF]
, 2013 Although nonnegative matrix factorization (NMF) is NP-hard in general, it has been shown very recently that it is tractable under the assumption that the input nonnegative data matrix is close to being separable (separability requires that all columns of
Gillis, Nicolas
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A Reliable Muscle Synergy Extraction Method based on Multivariate Curve Resolution-Alternating Least Squares [PDF]
E3S Web of Conferences, 2021 Muscle synergy is an important approach to evaluate motor function for patients with neurological diseases. Nonnegative matrix factorization (NMF) is the most widely used muscle synergy extraction method from electromyography (EMG) data.
Ma Yehao +4 more
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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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Complexity, 2019 Nonnegative Matrix Factorization (NMF) is a significant big data analysis technique. However, standard NMF regularized by simple graph does not have discriminative function, and traditional graph models cannot accurately reflect the problem of ...
Yong-Jing Hao +4 more
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Generalized Separable Nonnegative Matrix Factorization
, 2019 Nonnegative matrix factorization (NMF) is a linear dimensionality technique for nonnegative data with applications such as image analysis, text mining, audio source separation and hyperspectral unmixing.
Gillis, Nicolas, Pan, Junjun
core +1 more source
A deep matrix factorization method for learning attribute representations [PDF]
, 2015 Semi-Non-negative Matrix Factorization is a technique that learns a low-dimensional representation of a dataset that lends itself to a clustering interpretation. It is possible that the mapping between this new representation and our original data matrix
Bousmalis, Konstantinos +3 more
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Robust Semisupervised Nonnegative Local Coordinate Factorization for Data Representation
Complexity, 2018 Obtaining an optimum data representation is a challenging issue that arises in many intellectual data processing techniques such as data mining, pattern recognition, and gene clustering.
Wei Jiang +4 more
doaj +1 more source
Randomized Nonnegative Matrix Factorization
, 2018 Nonnegative matrix factorization (NMF) is a powerful tool for data mining. However, the emergence of `big data' has severely challenged our ability to compute this fundamental decomposition using deterministic algorithms. This paper presents a randomized
Erichson, N. Benjamin +3 more
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