Results 261 to 270 of about 23,571 (300)

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.
Jan Platos, Petr Gajdos, Pavel Krömer, Václav Snásel   +3 more
openaire   +1 more source

Novel Algorithm for Non-Negative Matrix Factorization

New Mathematics and Natural Computation, 2015
Non-negative matrix factorization (NMF) is an emerging technique with a wide spectrum of potential applications in data analysis. Mathematically, NMF can be formulated as a minimization problem with non-negative constraints. This problem attracts much attention from researchers for theoretical reasons and for potential applications.
Tran Dang Hien, Do Van Tuan, Pham Van At, Le Hung Son   +3 more
openaire   +2 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
Franco Woolfe, Michael J. Gerdes, Musodiq O. Bello, Xiaodong Tao, Ali Can   +4 more
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 ...
Kaoutar Benlamine, Nistor Grozavu, Younès Bennani, Basarab Matei   +3 more
openaire   +1 more source

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 ...
Daoqiang Zhang, Zhi-Hua Zhou, Songcan Chen   +2 more
openaire   +1 more source

Initialization enhancer for non-negative matrix factorization

Engineering Applications of Artificial Intelligence, 2007
Non-negative matrix factorization (NMF), proposed recently by Lee and Seung, has been applied to many areas such as dimensionality reduction, image classification image compression, and so on. Based on traditional NMF, researchers have put forward several new algorithms to improve its performance.
Zhonglong Zheng, Jie Yang 0002, Yitan Zhu   +2 more
openaire   +1 more source

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 0009, Kay Chen Tan   +3 more
openaire   +2 more sources

Rank-Adaptive Non-Negative Matrix Factorization

Cognitive Computation, 2018
Dimension reduction is a challenge task in data processing, especially in high-dimensional data processing area. Non-negative matrix factorization (NMF), as a classical dimension reduction method, has a contribution to the parts-based representation for the characteristics of non-negative constraints in the NMF algorithm.
Dong Shan, Xinzheng Xu, Tianming Liang, Shifei Ding   +3 more
openaire   +1 more source

Convex Non-negative Matrix Factorization in the Wild

2009 Ninth IEEE International Conference on Data Mining, 2009
Non-negative matrix factorization (NMF) has recently received a lot of attention in data mining, information retrieval, and computer vision. It factorizes a non-negative input matrix V into two non-negative matrix factors V = WH such that W describes "clusters" of the datasets.
Christian Thurau, Kristian Kersting, Christian Bauckhage   +2 more
openaire   +1 more source

Bayesian extensions of non-negative matrix factorization

2010 2nd International Workshop on Cognitive Information Processing, 2010
Although non-negative matrix factorization has become a popular data analysis tool for non-negative data sets, there are still some issues remaining partly unsolved. We investigate the potential of Bayesian techniques towards the solution of two important open questions concerning uniqueness and actual number of sources underlying the data. We derive a
Reinhard Schachtner, Gerhard Pöppel, Elmar Wolfgang Lang   +2 more
openaire   +1 more source