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   Articles with "nonnegative matrix" as a keyword



Adaptive local learning regularized nonnegative matrix factorization for data clustering

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recommendations! Published in 2018 at "Applied Intelligence"

DOI: 10.1007/s10489-018-1380-2

Abstract: Data clustering aims to group the input data instances into certain clusters according to the high similarity to each other, and it could be regarded as a fundamental and essential immediate or intermediate task that… read more here.

Keywords: adaptive local; matrix; data clustering; matrix factorization ... See more keywords

A unified global convergence analysis of multiplicative update rules for nonnegative matrix factorization

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recommendations! Published in 2018 at "Computational Optimization and Applications"

DOI: 10.1007/s10589-018-9997-y

Abstract: Multiplicative update rules are a well-known computational method for nonnegative matrix factorization. Depending on the error measure between two matrices, various types of multiplicative update rules have been proposed so far. However, their convergence properties… read more here.

Keywords: multiplicative update; update; nonnegative matrix; update rules ... See more keywords

On solving a revised model of the nonnegative matrix factorization problem by the modified adaptive versions of the Dai–Liao method

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recommendations! Published in 2024 at "Numerical Algorithms"

DOI: 10.1007/s11075-024-01886-w

Abstract: We suggest a revised form of a classic measure function to be employed in the optimization model of the nonnegative matrix factorization problem. More exactly, using sparse matrix approximations, the revision term is embedded to… read more here.

Keywords: matrix; factorization; model nonnegative; dai liao ... See more keywords

An augmented Lagrangian alternating direction method for overlapping community detection based on symmetric nonnegative matrix factorization

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recommendations! Published in 2020 at "International Journal of Machine Learning and Cybernetics"

DOI: 10.1007/s13042-019-00980-z

Abstract: In this paper, we present an augmented Lagrangian alternating direction algorithm for symmetric nonnegative matrix factorization. The convergence of the algorithm is also proved in detail and strictly. Then we present a modified overlapping community… read more here.

Keywords: matrix factorization; community detection; symmetric nonnegative; nonnegative matrix ... See more keywords
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Randomized Algorithms for Orthogonal Nonnegative Matrix Factorization

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recommendations! Published in 2020 at "Journal of the Operations Research Society of China"

DOI: 10.1007/s40305-020-00322-9

Abstract: Orthogonal nonnegative matrix factorization (ONMF) is widely used in blind image separation problem, document classification, and human face recognition. The model of ONMF can be efficiently solved by the alternating direction method of multipliers and… read more here.

Keywords: orthogonal nonnegative; nonnegative matrix; matrix factorization; randomized algorithms ... See more keywords

Accelerated SVD-based initialization for nonnegative matrix factorization

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recommendations! Published in 2024 at "Computational and Applied Mathematics"

DOI: 10.1007/s40314-024-02905-1

Abstract: Nonnegative matrix factorization (NMF) is a popular dimensionality reduction technique. NMF is typically cast as a non-convex optimization problem solved via standard iterative schemes, such as coordinate descent methods. Hence the choice of the initialization… read more here.

Keywords: matrix; factorization; svd based; matrix factorization ... See more keywords

Hyperspectral image restoration using framelet-regularized low-rank nonnegative matrix factorization

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recommendations! Published in 2018 at "Applied Mathematical Modelling"

DOI: 10.1016/j.apm.2018.06.044

Abstract: Abstract Hyperspectral image (HSI) restoration is a process to remove a mixture of various kinds of noise, which is a key preprocessing step to improve the performance of subsequent applications. Since the HSI has a… read more here.

Keywords: restoration; nonnegative matrix; matrix factorization; low rank ... See more keywords

Nonnegative realizability with Jordan structure

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recommendations! Published in 2020 at "Linear Algebra and its Applications"

DOI: 10.1016/j.laa.2019.11.016

Abstract: Abstract A general method is given for merging blocks in the Jordan canonical form of a nonnegative matrix. As a consequence, results, more general than any prior ones, are given for the universal realizability of… read more here.

Keywords: nonnegative realizability; jordan; realizability jordan; jordan structure ... See more keywords

Image multi-label annotation based on supervised nonnegative matrix factorization with new matching measurement

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recommendations! Published in 2017 at "Neurocomputing"

DOI: 10.1016/j.neucom.2016.09.052

Abstract: Nonnegative Matrix Factorization (NMF) has been attracting many scholars in the fields of pattern recognition and data mining to study it since its inception. To date, a large number of variant methods have been proposed… read more here.

Keywords: image; label annotation; matching measurement; annotation ... See more keywords

A hybrid algorithm for low-rank approximation of nonnegative matrix factorization

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recommendations! Published in 2019 at "Neurocomputing"

DOI: 10.1016/j.neucom.2019.07.059

Abstract: Abstract Nonnegative matrix factorization (NMF) is a recently developed method for data analysis. So far, most of known algorithms for NMF are based on alternating nonnegative least squares (ANLS) minimization of the squared Euclidean distance… read more here.

Keywords: low rank; rank approximation; algorithm; nonnegative matrix ... See more keywords

A framework for least squares nonnegative matrix factorizations with Tikhonov regularization

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recommendations! Published in 2020 at "Neurocomputing"

DOI: 10.1016/j.neucom.2019.12.103

Abstract: Abstract Nonnegative matrix factorization (NMF) is widely used for dimensionality reduction, clustering and signal unmixing. This paper presents a generic model for least squares NMFs with Tikhonov regularization, which covers many well-known NMF models as… read more here.

Keywords: framework; tikhonov regularization; nonnegative matrix; least squares ... See more keywords