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Hyperspectral signal unmixing based on constrained non-negative matrix factorization approach

Authors:

Lifu ZhangAuthors Info & Claims

Neurocomputing, Volume 204, Issue C

Pages 153 - 161

https://doi.org/10.1016/j.neucom.2015.10.132

Published: 05 September 2016 Publication History

Abstract

Hyperspectral unmixing is a hot topic in signal and image processing. A set of high-dimensional data matrices can be decomposed into two sets of non-negative low-dimensional matrices by Non-negative matrix factorization (NMF). However, the algorithm has many local solutions because of the non-convexity of the objective function. Some algorithms solve this problem by adding auxiliary constraints, such as sparse. The sparse NMF has a good performance but the result is unstable and sensitive to noise. Using the structural information for the unmixing approaches can make the decomposition stable. Someone used a clustering based on Euclidean distance to guide the decomposition and obtain good performance. The Euclidean distance is just used to measure the straight line distance of two points. However, the ground objects usually obey certain statistical distribution. It's difficult to measure the difference between the statistical distributions comprehensively by Euclidean distance. Kullback-Leibler divergence (KL divergence) is a better metric. In this paper, we propose a new approach named KL divergence constrained NMF which measures the statistical distribution difference using KL divergence instead of the Euclidean distance. It can improve the accuracy of structured information by using the KL divergence in the algorithm. Experimental results based on synthetic and real hyperspectral data show the superiority of the proposed algorithm with respect to other state-of-the-art algorithms.

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Cited By

Song GNg MJiang T(2023)Tangent Space Based Alternating Projections for Nonnegative Low Rank Matrix ApproximationIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2022.322405235:11(11917-11934)Online publication date: 1-Nov-2023
https://dl.acm.org/doi/10.1109/TKDE.2022.3224052
Shu ZSun YTang JYou C(2022)Adaptive Graph Regularized Deep Semi-nonnegative Matrix Factorization for Data RepresentationNeural Processing Letters10.1007/s11063-022-10882-x54:6(5721-5739)Online publication date: 1-Dec-2022
https://dl.acm.org/doi/10.1007/s11063-022-10882-x
He KPeng YLiu SLi J(2020)Regularized Negative Label Relaxation Least Squares Regression for Face RecognitionNeural Processing Letters10.1007/s11063-020-10219-651:3(2629-2647)Online publication date: 1-Jun-2020
https://dl.acm.org/doi/10.1007/s11063-020-10219-6
Show More Cited By

Hyperspectral signal unmixing based on constrained non-negative matrix factorization approach
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Unsupervised learning

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Published In

cover image Neurocomputing

Neurocomputing Volume 204, Issue C

September 2016

240 pages

ISSN:0925-2312

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Copyright © Elsevier B.V.

Publisher

Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 05 September 2016

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Cited By

Song GNg MJiang T(2023)Tangent Space Based Alternating Projections for Nonnegative Low Rank Matrix ApproximationIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2022.322405235:11(11917-11934)Online publication date: 1-Nov-2023
https://dl.acm.org/doi/10.1109/TKDE.2022.3224052
Shu ZSun YTang JYou C(2022)Adaptive Graph Regularized Deep Semi-nonnegative Matrix Factorization for Data RepresentationNeural Processing Letters10.1007/s11063-022-10882-x54:6(5721-5739)Online publication date: 1-Dec-2022
https://dl.acm.org/doi/10.1007/s11063-022-10882-x
He KPeng YLiu SLi J(2020)Regularized Negative Label Relaxation Least Squares Regression for Face RecognitionNeural Processing Letters10.1007/s11063-020-10219-651:3(2629-2647)Online publication date: 1-Jun-2020
https://dl.acm.org/doi/10.1007/s11063-020-10219-6
Wang ZZhu RFukui KXue J(2018)Cone-based joint sparse modelling for hyperspectral image classificationSignal Processing10.1016/j.sigpro.2017.11.001144:C(417-429)Online publication date: 1-Mar-2018
https://dl.acm.org/doi/10.1016/j.sigpro.2017.11.001
Fang LZhuo HLi S(2018)Super-resolution of hyperspectral image via superpixel-based sparse representationNeurocomputing10.1016/j.neucom.2017.08.019273:C(171-177)Online publication date: 17-Jan-2018
https://dl.acm.org/doi/10.1016/j.neucom.2017.08.019
Peng YLi LLiu SLi JWang X(2018)Extended sparse representation-based classification method for face recognitionMachine Vision and Applications10.1007/s00138-018-0941-z29:6(991-1007)Online publication date: 1-Aug-2018
https://dl.acm.org/doi/10.1007/s00138-018-0941-z
Zhu XZhang Z(2017)Improved self-paced learning framework for nonnegative matrix factorizationPattern Recognition Letters10.1016/j.patrec.2017.06.01697:C(1-7)Online publication date: 1-Oct-2017
https://dl.acm.org/doi/10.1016/j.patrec.2017.06.016
Spurek PTabor JRola POciepka M(2017)ICA based on asymmetryPattern Recognition10.1016/j.patcog.2017.02.01967:C(230-244)Online publication date: 1-Jul-2017
https://dl.acm.org/doi/10.1016/j.patcog.2017.02.019

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