Results 21 to 30 of about 7,782 (177)
Causal Association Between Immune Cell Traits and Risk of Multiple Malignant and Nonmalignant CNS Diseases: A Mendelian Randomization and Single-Cell Transcriptomic Analysis. [PDF]
Brain BehavThis is the graph abstract of our manuscript delineating the methods employed in both MR and scRNA‐seq analyses within the present investigation. ABSTRACT Background The influence of immune cell traits (ICTs) on the onset of multiple brain diseases has been previously investigated; however, it is limited by the sample size or colocalization evidence ...
Ke S, Yan J, Li B, Feng X.
europepmc +2 more sources
Jisuanji kexue yu tansuo, 2020 In order to handle semi-supervised clustering scenarios where only part of the pairwise constraint information is available in the target dataset, on the basis of nonnegative matrix factorization (NMF) architecture, this paper proposes a nonnegative ...
CAO Jiawei, QIAN Pengjiang
doaj +1 more source
Single channel speech music separation using nonnegative matrix factorization and spectral masks [PDF]
, 2011 A single channel speech-music separation algorithm based on nonnegative matrix factorization (NMF) with spectral masks is proposed in this work. The proposed algorithm uses training data of speech and music signals with nonnegative matrix factorization ...
Erdogan, Hakan +2 more
core +1 more source
IEEE Access, 2018 Nonnegative matrix factorization (NMF) technique has been developed successfully to represent the intuitively meaningful feature of data. A suitable representation can faithfully preserve the intrinsic structure of data.
Wenjie Zhu, Yunhui Yan
doaj +1 more source
Frontiers in Genetics, 2021 Advances in single cell transcriptomics have allowed us to study the identity of single cells. This has led to the discovery of new cell types and high resolution tissue maps of them.
Sooyoun Oh, Haesun Park, Xiuwei Zhang
doaj +1 more source
Discriminative and Graph Regularized Nonnegative Matrix Factorization with Kernel Method
Jisuanji kexue yu tansuo, 2020 Nonnegative matrix factorization (NMF) is a popular technique for dimension reduction,which has been extensively applied in image clustering and other fields.However,NMF is an unsupervised approach,which does not take the label information of the data ...
LI Xiangli, ZHANG Ying
doaj +1 more source
Sparse Dual Graph-Regularized Deep Nonnegative Matrix Factorization for Image Clustering
IEEE Access, 2021 Deep nonnegative matrix factorization (Deep NMF) as an emerging technique for image clustering has attracted more and more attention. This is because it can effectively reduce high-dimensional data and reveal the latent hierarchical information of the ...
Weiyu Guo
doaj +1 more source
Nonnegative Matrix Factorization With Data-Guided Constraints For Hyperspectral Unmixing
Remote Sensing, 2017 Hyperspectral unmixing aims to estimate a set of endmembers and corresponding abundances in pixels. Nonnegative matrix factorization (NMF) and its extensions with various constraints have been widely applied to hyperspectral unmixing.
Risheng Huang, Xiaorun Li, Liaoying Zhao
doaj +1 more source
Entropy, 2017 Semi-Nonnegative Matrix Factorization (Semi-NMF), as a variant of NMF, inherits the merit of parts-based representation of NMF and possesses the ability to process mixed sign data, which has attracted extensive attention. However, standard Semi-NMF still
Peng Luo, Jinye Peng
doaj +1 more source
Nonnegative matrix factorization requires irrationality [PDF]
, 2017 Nonnegative matrix factorization (NMF) is the problem of decomposing a given nonnegative n × m matrix M into a product of a nonnegative n × d matrix W and a nonnegative d × m matrix H. A longstanding open question, posed by Cohen and Rothblum in 1993, is
Chistikov, Dmitry +4 more
core +2 more sources