Results 251 to 260 of about 23,571 (300)

Review of Patient Gene Profiles Obtained through a Non-Negative Matrix Factorization-Based Framework to Determine the Role Inflammation Plays in Neuroblastoma Pathogenesis. [PDF]

Int J Mol Sci
Boccarelli A, Del Buono N, Esposito F.
europepmc   +1 more source

Correction to: BANMF-S: a blockwise accelerated non-negative matrix factorization framework with structural network constraints for single cell imputation. [PDF]

Brief Bioinform
europepmc   +1 more source

Longitudinal Relationship Between Brain Atrophy Patterns, Cognitive Decline, and Cerebrospinal Fluid Biomarkers in Alzheimer's Disease Explored by Orthonormal Projective Non-Negative Matrix Factorization. [PDF]

J Alzheimers Dis
Shui L, Shibata D, Chan KCG, Zhang W, Sung J, Haynor DR, Alzheimer’s Disease Neuroimaging Initiative.   +6 more
europepmc   +1 more source

Multichannel high resolution NMF for modelling convolutive mixtures of non-stationary signals in the time-frequency domain

, 2013
Badeau, Roland, Plumbley, Mark
core  

Accelerated spike-triggered non-negative matrix factorization reveals coordinated ganglion cell subunit mosaics in the primate retina

Zapp SJ, Khani MH, Schreyer HM, Sridhar S, Ramakrishna V, Krüppel S, Mietsch M, Protti DA, Karamanlis D, Gollisch T.   +9 more
europepmc   +1 more source

Non-Negative Matrix Factorization with Constraints

Proceedings of the AAAI Conference on Artificial Intelligence, 2010
Non-negative matrix factorization (NMF), as a useful decomposition method for multivariate data, has been widely used in pattern recognition, information retrieval and computer vision. NMF is an effective algorithm to find the latent structure of the data and leads to a parts-based representation.
Haifeng Liu 0001, Zhaohui Wu 0001
openaire   +2 more sources

Graph Regularized Non-Negative Low-Rank Matrix Factorization for Image Clustering

IEEE Transactions on Cybernetics, 2017
Non-negative matrix factorization (NMF) has been one of the most popular methods for feature learning in the field of machine learning and computer vision.
Xuelong Li, Guosheng Cui, Yongsheng Dong   +2 more
exaly   +2 more sources

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Dropout non-negative matrix factorization

Knowledge and Information Systems, 2018
Non-negative matrix factorization (NMF) has received lots of attention in research communities like document clustering, image analysis, and collaborative filtering. However, NMF-based approaches often suffer from overfitting and interdependent features which are caused by latent feature co-adaptation during the learning process.
Zhicheng He 0001, Jie Liu 0007, Caihua Liu, Yuan Wang, Airu Yin, Yalou Huang   +5 more
openaire   +1 more source

Farness preserving Non-negative matrix factorization

2014 IEEE International Conference on Image Processing (ICIP), 2014
Dramatic growth in the volume of data made a compact and informative representation of the data highly demanded in computer vision, information retrieval, and pattern recognition. Non-negative Matrix Factorization (NMF) is used widely to provide parts-based representations by factorizing the data matrix into non-negative matrix factors.
Babaee, Mohammadreza, Bahmanyar, Reza, Rigoll, Gerhard, Datcu, Mihai   +3 more
openaire   +1 more source