Adaptive local learning regularized nonnegative matrix factorization for data clustering
Sign Up to like & getrecommendations! 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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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
An augmented Lagrangian alternating direction method for overlapping community detection based on symmetric nonnegative matrix factorization
Sign Up to like & getrecommendations! 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
Randomized Algorithms for Orthogonal Nonnegative Matrix Factorization
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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
Hyperspectral image restoration using framelet-regularized low-rank nonnegative matrix factorization
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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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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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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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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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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
Robust nonnegative matrix factorization with structure regularization
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DOI: 10.1016/j.neucom.2020.06.049
Abstract: Abstract Nonnegative matrix factorization (NMF) has attracted more and more attention due to its wide applications in computer vision, information retrieval, and machine learning. In contrast to the original NMF and its variants, this paper… read more here.
Keywords: matrix; structure; nonnegative matrix; matrix factorization ... See more keywords
On equivalence of likelihood maximization of stochastic block model and constrained nonnegative matrix factorization
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DOI: 10.1016/j.physa.2018.02.068
Abstract: Community structures detection in complex network is important for understanding not only the topological structures of the network, but also the functions of it. Stochastic block model and nonnegative matrix factorization are two widely used… read more here.
Keywords: model; nonnegative matrix; matrix factorization; stochastic block ... See more keywords