著名的科学杂志《Nature》于1999年刊登了两位科学家D.D.Lee和H.S.Seung对数学中非负矩阵研究的突出成果。该文提出了一种新的矩阵分解思想――非负矩阵分解(Non-negative Matrix Factorization,NMF)算法,即NMF是在矩阵中所有元素均为非负数约束条件之下的矩阵分解方法。该论文的发表迅速引起了各个领域中的科学研究人...
Lee, D. and Seung, H.S., 2000. Algorithms fornon-negative matrix factorization. Advances in neural information processing systems, 13, pp.556-562. COMP6245 Foundation of Machine Learning Module of University of Southampton
pyDNMFk: Python Distributed Non Negative Matrix Factorization with determination of hidden featurespyDNMFk is a software package for applying non-negative matrix factorization in a distributed fashion to large datasets. It can minimize the difference between reconstructed data and the original data through...
基于非负矩阵分解Non-negative Matrix Factorization的数据生成方法研究(Matlab代码实现) 摘要 1. 引言 2. 非负矩阵分解(NMF)基础 2.1 定义与原理 2.2 算法实现 2.3 特点与优势 3. 基于NMF的数据增强方法 3.1 方法概述 3.2 应用案例 4. 实验与评估 5. 结论与展望 2 运行结果 3 参考文献 4 Matlab代码实现 ...
Hoss Belyadi, Alireza Haghighat, in Machine Learning Guide for Oil and Gas Using Python, 2021 Nonnegative matrix factorization (NMF) The main goal in NMF is to decompose a matrix into two matrices. NMF is a matrix factorization technique. As was previously discussed, PCA creates factors that ...
Nimfa is a Python module that implements many algorithms for nonnegative matrix factorization. Nimfa is distributed under the BSD license. The project was started in 2011 by Marinka Zitnik as a Google Summer of Code project, and since then many volunteers have contributed. See AUTHORS file for ...
NIMFA: A Python Library for Nonnegative Matrix FactorizationComputer Science - Machine LearningMarinka ZitnikBlaz ZupanarXivZitnik M, Zupan B. NIMFA: A Python Library for Nonnegative Matrix Factorization. J Mach Learn Res. 2012;13:849-53....
Nonnegative matrix factorization (NMF) is widely used to analyze high-dimensional count data because, in contrast to real-valued alternatives such as factor analysis, it produces an interpretable parts-based representation. However, in applications such as spatial transcriptomics, NMF fails to incorporat...
We introduce a new method based on nonnegative matrix factorization, Neural NMF, for detecting latent hierarchical structure in data. Datasets with hierarchical structure arise in a wide variety of fields, such as document classification, image processing, and bioinformatics. Neural NMF recursively appli...
we use spatially-mapped Raman spectra of mixtures of chirality-sorted single walled carbon nanotubes dispersed sparsely on flat silicon/silicon oxide substrates. We use non-negative matrix factorization (NMF) decomposition in scikit-learn, an open-source, python language “machine learning” package, ...