Results 11 to 20 of about 82,948 (222)
Randomized Nonnegative Matrix Factorization [PDF]
Pattern Recognition Letters, 2018 Nonnegative matrix factorization (NMF) is a powerful tool for data mining. However, the emergence of `big data' has severely challenged our ability to compute this fundamental decomposition using deterministic algorithms. This paper presents a randomized
Erichson, N. Benjamin +3 more
core +2 more sources
Log-based sparse nonnegative matrix factorization for data representation. [PDF]
Knowl Based Syst, 2022 Nonnegative matrix factorization (NMF) has been widely studied in recent years due to its effectiveness in representing nonnegative data with parts-based representations. For NMF, a sparser solution implies better parts-based representation.
Peng C +5 more
europepmc +3 more sources
UINMF performs mosaic integration of single-cell multi-omic datasets using nonnegative matrix factorization. [PDF]
Nat Commun, 2022 Single-cell genomic technologies provide an unprecedented opportunity to define molecular cell types in a data-driven fashion, but present unique data integration challenges.
Kriebel AR, Welch JD.
europepmc +2 more sources
Generalized Separable Nonnegative Matrix Factorization [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 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
core +3 more sources
Sparse Deep Nonnegative Matrix Factorization [PDF]
Big Data Mining and Analytics, 2020 Nonnegative Matrix Factorization (NMF) is a powerful technique to perform dimension reduction and pattern recognition through single-layer data representation learning. However, deep learning networks, with their carefully designed hierarchical structure,
Zhenxing Guo, Shihua Zhang
doaj +3 more sources
Computing a Nonnegative Matrix Factorization -- Provably [PDF]
SIAM Journal on Computing, 2011 In the Nonnegative Matrix Factorization (NMF) problem we are given an $n \times m$ nonnegative matrix $M$ and an integer $r > 0$. Our goal is to express $M$ as $A W$ where $A$ and $W$ are nonnegative matrices of size $n \times r$ and $r \times m ...
Arora, Sanjeev +3 more
core +4 more sources
Predicting epileptic seizures using nonnegative matrix factorization. [PDF]
PLoS ONE, 2020 This paper presents a procedure for the patient-specific prediction of epileptic seizures. To this end, a combination of nonnegative matrix factorization (NMF) and smooth basis functions with robust regression is applied to power spectra of intracranial ...
Olivera Stojanović +2 more
doaj +2 more sources
Descent methods for Nonnegative Matrix Factorization [PDF]
, 2008 In this paper, we present several descent methods that can be applied to nonnegative matrix factorization and we analyze a recently developped fast block coordinate method called Rank-one Residue Iteration (RRI).
Blondel, Vincent D. +2 more
core +5 more sources
Graph Regularized Nonnegative Matrix Factorization for Data Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011 Deng Cai +3 more
semanticscholar +3 more sources
A Provable Splitting Approach for Symmetric Nonnegative Matrix Factorization [PDF]
IEEE Transactions on Knowledge and Data Engineering, 2023 The symmetric Nonnegative Matrix Factorization (NMF), a special but important class of the general NMF, has found numerous applications in data analysis such as various clustering tasks.
Xiao Li, Zhihui Zhu, Qiuwei Li, Kai Liu
semanticscholar +1 more source