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
Two-Dimensional Semi-Nonnegative Matrix Factorization for Clustering. [PDF]
Inf Sci (N Y), 2022 In this paper, we propose a new Semi-Nonnegative Matrix Factorization method for 2-dimensional (2D) data, named TS-NMF. It overcomes the drawback of existing methods that seriously damage the spatial information of the data by converting 2D data to ...
Peng C +4 more
europepmc +3 more sources
Localized semi-nonnegative matrix factorization (LocaNMF) of widefield calcium imaging data. [PDF]
PLoS Comput Biol, 2020 Widefield calcium imaging enables recording of large-scale neural activity across the mouse dorsal cortex. In order to examine the relationship of these neural signals to the resulting behavior, it is critical to demix the recordings into meaningful ...
Saxena S +10 more
europepmc +2 more sources
Monotonicity of the number of positive entries in nonnegative matrix powers [PDF]
Journal of Inequalities and Applications, 2018 Let A be a nonnegative matrix of order n and f(A) $f(A)$ denote the number of positive entries in A. We prove that if f(A)≤3 $f(A)\leq3$ or f(A)≥n2−2n+2 $f(A)\geq n^{2}-2n+2$, then the sequence {f(Ak)}k=1∞ $\{f(A^{k})\}_{k=1}^{\infty}$ is monotonic for ...
Qimiao Xie
doaj +2 more sources
Graph Regularized Nonnegative Matrix Factorization for Data Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011 Deng Cai, Xiaofei He, Jiawei Han
exaly +2 more sources
Image Clustering Algorithm Based on Hypergraph Regularized Nonnegative Tucker Decomposition [PDF]
Jisuanji gongcheng, 2022 The internal geometry structure of high-dimensional data is ignored when nonnegative tensor decomposition is applied to image clustering.To solve this problem, we propose a Hypergraph regularized Nonnegative Tucker Decomposition(HGNTD) model by adding a ...
CHEN Luyao, LIU Qilong, XU Yunxia, CHEN Zhen
doaj +1 more source
Contrastive Deep Nonnegative Matrix Factorization For Community Detection [PDF]
IEEE International Conference on Acoustics, Speech, and Signal Processing, 2023 Recently, nonnegative matrix factorization (NMF) has been widely adopted for community detection, because of its better interpretability. However, the existing NMF-based methods have the following three problems: 1) they directly transform the original ...
Yuecheng Li +4 more
semanticscholar +1 more source
Hyperspectral Unmixing Based on Nonnegative Matrix Factorization: A Comprehensive Review [PDF]
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022 Hyperspectral unmixing has been an important technique that estimates a set of endmembers and their corresponding abundances from a hyperspectral image (HSI).
Xin-Ru Feng +5 more
semanticscholar +1 more source
Robust Structured Convex Nonnegative Matrix Factorization for Data Representation
IEEE Access, 2021 Nonnegative Matrix Factorization (NMF) is a popular technique for machine learning. Its power is that it can decompose a nonnegative matrix into two nonnegative factors whose product well approximates the nonnegative matrix.
Qing Yang +3 more
doaj +1 more source