Cluster-Wise Weighted NMF for Hyperspectral Images Unmixing with Imbalanced Data
Remote Sensing, 2021 Hyperspectral unmixing is an important technique for analyzing remote sensing images which aims to obtain a collection of endmembers and their corresponding abundances.
Xiaochen Lv, Wenhong Wang, Hongfu Liu
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Clustering NMF basis functions using Shifted NMF for monaural sound source separation
2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2011 Non-negative Matrix Factorization (NMF) has found use in single channel separation of audio signals, as it gives a parts-based decomposition of audio spectrograms where the parts typically correspond to individual notes or chords. However, a notable shortcoming of NMF is the need to cluster the basis functions to their sources after decomposition ...
Jaiswal, Rajesh +4 more
openaire +3 more sources
Nonconvex Nonseparable Sparse Nonnegative Matrix Factorization for Hyperspectral Unmixing
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020 Hyperspectral unmixing is an important step to learn the material categories and corresponding distributions in a scene. Over the past decade, nonnegative matrix factorization (NMF) has been utilized for this task, thanks to its good physical ...
Fengchao Xiong +3 more
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Skin Barrier Biomarkers in Patch-Induced and Clinical Allergic and Irritant Contact Dermatitis. [PDF]
Contact DermatitisBoth patch‐induced and clinical ICD and ACD showed consistent alterations in skin barrier‐associated biomarkers, indicative of skin barrier impairment. Among the tested allergens, chromium elicited more pronounced biomarker responses compared to nickel and methylisothiazolinone.
Kezic S +8 more
europepmc +2 more sources
Underwater acoustic target signal enhancement algorithm optimized by feature preservation and noise update [PDF]
Zhihui kongzhi yu fangzhenThe enhancement effect of the classic Nonnegative Matrix Factorization (NMF) applied to underwater acoustic target signal is unsatisfactory for the feature overlap of underwater acoustic target signal and the variability of ocean underwater acoustic ...
XIAO Haixia, CUI Shuangyue, LI Dawei, SUN Mingming, LIU Xianzhong, YANG Zhenxin
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Robust Semi-Supervised Non-Negative Matrix Factorization With Structured Normalization
IEEE Access, 2019 Non-negative matrix factorization (NMF) approximates a non-negative data matrix with the product of two low-rank non-negative matrices by minimizing the cost of such approximation.
Liujing Wang +4 more
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Hessian Regularization Based Factorization Algorithm Combining Multi-view and Non-negative Matrix [PDF]
Jisuanji gongcheng, 2017 Non-negative matrix does not consider the manifold of data when represents multi-view data,which results in the ineffective express of the data internal expression.In this paper,Hessian regularized Non-negative Matrix Factorization(NMF) is proposed.By ...
WANG Chaofeng,SHI Jun,WU Jinjie,ZHU Jie
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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
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Algorithms for nonnegative matrix factorization with the beta-divergence [PDF]
, 2011 This paper describes algorithms for nonnegative matrix factorization (NMF) with the beta-divergence (beta-NMF). The beta-divergence is a family of cost functions parametrized by a single shape parameter beta that takes the Euclidean distance, the ...
Févotte, Cédric, Idier, Jérôme
core +5 more sources
Unsupervised Low Latency Speech Enhancement With RT-GCC-NMF [PDF]
IEEE Journal on Selected Topics in Signal Processing, 2019 In this paper, we present RT-GCC-NMF: a real-time (RT), two-channel blind speech enhancement algorithm that combines the non-negative matrix factorization (NMF) dictionary learning algorithm with the generalized cross-correlation (GCC) spatial ...
S. Wood, J. Rouat
semanticscholar +1 more source