Results 281 to 290 of about 112,822 (316)

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.
Jie Liu, Yuan Wang, Caihua Liu, Zhicheng He, Airu Yin, Yalou Huang   +5 more
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Non-negative Matrix Factorization on Kernels

2006
In this paper, we extend the original non-negative matrix factorization (NMF) to kernel NMF (KNMF). The advantages of KNMF over NMF are: 1) it could extract more useful features hidden in the original data through some kernel-induced nonlinear mappings; 2) it can deal with data where only relationships (similarities or dissimilarities) between objects ...
Zhi-Hua Zhou, Daoqiang Zhang, Songcan Chen   +2 more
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Non-negative matrix factorization for EEG

2013 The International Conference on Technological Advances in Electrical, Electronics and Computer Engineering (TAEECE), 2013
Today with the progress of science and technology becomes signal analysis, data analysis and data mining are very Important in most science and engineering applications. Extracting useful knowledge from experimental raw datasets, measurements, observations and analysis and understand complex data has become very important challenge in the world.
Ibrahim Salem Jahan, Vaclav Snasel
openaire   +2 more sources

Multiobjective Sparse Non-Negative Matrix Factorization

IEEE Transactions on Cybernetics, 2019
Non-negative matrix factorization (NMF) is becoming increasingly popular in many research fields due to its particular properties of semantic interpretability and part-based representation. Sparseness constraints are usually imposed on the NMF problems in order to achieve potential features and sparse representation.
Maoguo Gong, Xiangming Jiang, Hao Li, Kay Chen Tan   +3 more
openaire   +3 more sources

Autofluorescence Removal by Non-Negative Matrix Factorization

IEEE Transactions on Image Processing, 2011
This paper describes a new, physically interpretable, fully automatic algorithm for removal of tissue autofluorescence (AF) from fluorescence microscopy images, by non-negative matrix factorization. Measurement of signal intensities from the concentration of certain fluorescent reporter molecules at each location within a sample of biological tissue is
Ali Can, Xiaodong Tao, Musodiq Bello, Franco Woolfe, Michael J. Gerdes   +4 more
openaire   +3 more sources

Non-negative Matrix Factorization on GPU

2010
Today, the need of large data collection processing increase. Such type of data can has very large dimension and hidden relationships. Analyzing this type of data leads to many errors and noise, therefore, dimension reduction techniques are applied. Many techniques of reduction were developed, e.g. SVD, SDD, PCA, ICA and NMF.
Petr Gajdoš, Vaclav Snasel, Pavel Krömer, Jan Platos   +3 more
openaire   +2 more sources

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   +2 more sources

Swarm Intelligence for Non-Negative Matrix Factorization

International Journal of Swarm Intelligence Research, 2011
The Non-negative Matrix Factorization (NMF) is a special low-rank approximation which allows for an additive parts-based and interpretable representation of the data. This article presents efforts to improve the convergence, approximation quality, and classification accuracy of NMF using five different meta-heuristics based on swarm intelligence ...
Andreas Janecek, Ying Tan
openaire   +2 more sources

Non-Negative Matrix Factorization (NMF)

2015
In this chapter we introduce the Non-Negative Matrix Factorization (NMF), which is an unsupervised algorithm that projects data into lower dimensional spaces, effectively reducing the number of features while retaining the basis information necessary to reconstruct the original data.
Noel Lopes, Bernardete Ribeiro
openaire   +2 more sources

Collaborative Non-negative Matrix Factorization

2019
Non-negative matrix factorization is a machine learning technique that is used to decompose large data matrices imposing the non-negativity constraints on the factors. This technique has received a significant amount of attention as an important problem with many applications in different areas such as language modeling, text mining, clustering, music ...
Nistor Grozavu, Basarab Matei, Younès Bennani, Kaoutar Benlamine   +3 more
openaire   +2 more sources