Non-negative Matrix Factorization Based on Spectral Reconstruction Constraint for Hyperspectral and Panchromatic Image Fusion [PDF]
Jisuanji kexue, 2021 An effective algorithm for unmixing hyperspectral and panchromatic images of non-negative matrix factorization based on spectral reconstruction constraint is proposed.Firstly,this algorithm employs the regularization with minimum spectral reconstruction ...
GUAN Zheng, DENG Yang-lin, NIE Ren-can
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IET Image Processing, 2022 Benefiting from the good physical interpretations and low computational complexity, non‐negative matrix factorization (NMF) has attracted wide attentions in data representation learning tasks.
Yanfeng Sun +4 more
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Non-negative Matrix Factorization Parallel Optimization Algorithm Based on Lp-norm [PDF]
Jisuanji kexueNon-negative matrix factorization algorithm is an important tool for image clustering,data compression and feature extraction.Traditional non-negative matrix factorization algorithms mostly use Euclidean distance to measure reconstruction error,which has
HUANG Lulu, TANG Shuyu, ZHANG Wei, DAI Xiangguang
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Optimization and expansion of non-negative matrix factorization
BMC Bioinformatics, 2020 Background Non-negative matrix factorization (NMF) is a technique widely used in various fields, including artificial intelligence (AI), signal processing and bioinformatics.
Xihui Lin, Paul C. Boutros
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Majorization-Minimization Algorithm for Discriminative Non-Negative Matrix Factorization
IEEE Access, 2020 This paper proposes a basis training algorithm for discriminative non-negative matrix factorization (NMF) with applications to single-channel audio source separation.
Li Li, Hirokazu Kameoka, Shoji Makino
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Sample Complexity of Dictionary Learning and other Matrix Factorizations [PDF]
, 2015 Many modern tools in machine learning and signal processing, such as sparse dictionary learning, principal component analysis (PCA), non-negative matrix factorization (NMF), $K$-means clustering, etc., rely on the factorization of a matrix obtained by ...
Bach, Francis +4 more
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Optimal Recovery of Missing Values for Non-Negative Matrix Factorization
IEEE Open Journal of Signal Processing, 2021 Missing values imputation is often evaluated on some similarity measure between actual and imputed data. However, it may be more meaningful to evaluate downstream algorithm performance after imputation than the imputation itself.
Rebecca Chen Dean, Lav R. Varshney
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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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Face Recognition Based on Wavelet Kernel Non-Negative Matrix Factorization
Cybernetics and Information Technologies, 2014 In this paper a novel face recognition algorithm, based on wavelet kernel non-negative matrix factorization (WKNMF), is proposed. By utilizing features from multi-resolution analysis, the nonlinear mapping capability of kernel nonnegative matrix ...
Bai, Lin, Li Yanbo, Hui Meng
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MCA-NMF: Multimodal Concept Acquisition with Non-Negative Matrix Factorization. [PDF]
PLoS ONE, 2015 In this paper we introduce MCA-NMF, a computational model of the acquisition of multimodal concepts by an agent grounded in its environment. More precisely our model finds patterns in multimodal sensor input that characterize associations across ...
Olivier Mangin +3 more
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