Results 51 to 60 of about 53,454 (335)
Directional clustering through matrix factorization [PDF]
, 2016 This paper deals with a clustering problem where feature vectors are clustered depending on the angle between feature vectors, that is, feature vectors are grouped together if they point roughly in the same direction.
Blumensath, Thomas
core +1 more source
Mathematics, 2023 Graph regularized non-negative matrix factorization (GNMF) is widely used in feature extraction. In the process of dimensionality reduction, GNMF can retain the internal manifold structure of data by adding a regularizer to non-negative matrix ...
Minghua Wan, Mingxiu Cai, Guowei Yang
doaj +1 more source
A non-convex optimization framework for large-scale low-rank matrix factorization
Machine Learning with Applications, 2022 Low-rank matrix factorization problems such as non negative matrix factorization (NMF) can be categorized as a clustering or dimension reduction technique. The latter denotes techniques designed to find representations of some high dimensional dataset in
Sajad Fathi Hafshejani +3 more
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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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Initialization for non-negative matrix factorization: a comprehensive review [PDF]
International Journal of Data Science and Analysis, 2021 Non-negative matrix factorization (NMF) has become a popular method for representing meaningful data by extracting a non-negative basis feature from an observed non-negative data matrix.
Sajad Fathi Hafshejani, Z. Moaberfard
semanticscholar +1 more source
Theorems on Positive Data: On the Uniqueness of NMF [PDF]
, 2008 We investigate the conditions for which nonnegative matrix factorization (NMF) is unique and introduce several theorems which can determine whether the decomposition is in fact unique or not.
Pumbley, Mark +12 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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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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Multifrontal Non-negative Matrix Factorization
, 2020 Non-negative matrix factorization (Nmf) is an important tool in high-performance large scale data analytics with applications ranging from community detection, recommender system, feature detection and linear and non-linear unmixing. While traditional Nmf works well when the data set is relatively dense, however, it may not extract sufficient structure
Piyush Sao, Ramakrishnan Kannan
openaire +2 more sources
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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