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Investigating the feature extraction capabilities of non-negative matrix factorisation algorithms for black-and-white images [PDF]
ITM Web of ConferencesNonnegative matrix factorisation (NMF) is a class of matrix factorisation methods to approximate a nonnegative matrix as a product of two nonnegative matrices.
Liew How Hui +2 more
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NMF-SAE: An Interpretable Sparse Autoencoder for Hyperspectral Unmixing
, 2021 Hyperspectral unmixing is an important tool to learn the material constitution and distribution of a scene. Model-based unmixing methods depend on well-designed iterative optimization algorithms, which is usually time consuming.
Jun Zhou +9 more
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LS-NMF: A modified non-negative matrix factorization algorithm utilizing uncertainty estimates
BMC Bioinformatics, 2006 Background Non-negative matrix factorisation (NMF), a machine learning algorithm, has been applied to the analysis of microarray data. A key feature of NMF is the ability to identify patterns that together explain the data as a linear combination of ...
Kossenkov Andrew V +2 more
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IEEE Access, 2021 We analyzed muscle excitation estimation systematically by Non-negative matrix factorization (NMF) from surface electromyograms (EMG) during dynamic contractions of biceps brachii (BB) muscles.
Martin Savc, Ales Holobar
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The NMF problem and lattice-subspaces [PDF]
Linear Algebra and its Applications, 2020 Suppose that $A$ is a nonnegative $n\times m$ real matrix. The NMF problem is the determination of two nonnegative real matrices $F$, $V$ so that $A=FV$ with intermediate dimension $p$ smaller than $min\{ n,m\}$. In this article we present a general mathematical method for the determination of two nonnegative real factors $F,V$ of $A$. During the first
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Platelet-Related Molecular Subtype to Predict Prognosis in Hepatocellular Carcinoma
Journal of Hepatocellular Carcinoma, 2022 Genhao Zhang Department of Blood Transfusion, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, People’s Republic of ChinaCorrespondence: Genhao Zhang, Email wangshuya617@bjmu.edu.cnPurpose: Complex crosstalk between tumor cells and ...
Zhang G
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Applied Sciences, 2020 In this paper, methods to estimate the number of basis vectors of the nonnegative matrix factorization (NMF) of automatic music transcription (AMT) systems are proposed.
Seokjin Lee
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time-series-nmf: Non-negative Matrix Factorization for Time Series
, 2020 time-series-nmf is a Python package implementing non-negative matrix factorization for time series data. Currently, it supports a version with Tikhonov regularization and sparse constraints as proposed by Fabregat R.. et. al. and implemented in Matlab in
Valentina Staneva, Wu-Jung Lee
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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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CoRR, 2016 Nonnegative Matrix Factorization(NMF) is a common used technique in machine learning to extract features out of data such as text documents and images thanks to its natural clustering properties. In particular, it is popular in image processing since it can decompose several pictures and recognize common parts if they're located in the same position ...
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