Results 311 to 320 of about 142,276 (359)

Volume regularized non-negative matrix factorizations

2018 9th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2018
This work considers two volume regularized non-negative matrix factorization (NMF) problems that decompose a nonnegative matrix X into the product of two nonnegative matrices W and H with a regularization on the volume of the convex hull spanned by the columns of W.
M.S. Andersen Ang, Nicolas Gillis
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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, Zhaohui Wu
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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
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Uniqueness of non-negative matrix factorization [PDF]

2007 IEEE/SP 14th Workshop on Statistical Signal Processing, 2007
In this paper, two new properties of stochastic vectors are introduced and a strong uniqueness theorem on non-negative matrix factorizations (NMF) is introduced. It is described how the theorem can be applied to two of the common application areas of NMF, namely music analysis and probabilistic latent semantic analysis. Additionally, the theorem can be
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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
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Robust multi-view non-negative matrix factorization with adaptive graph and diversity constraints

Information Sciences, 2023
Chenglu Li, Hangjun Che, Man-Fai Leung, Cheng Liu, Zheng Yan   +4 more
semanticscholar   +1 more source

Robust discriminative non-negative matrix factorization

Neurocomputing, 2016
Traditional non-negative matrix factorization (NMF) is an unsupervised method that represents non-negative data by a part-based dictionary and non-negative codes. Recently, the unsupervised NMF has been extended to discriminative ones for classification problems.
Ruiqing Zhang, Zhenfang Hu, Gang Pan, Yueming Wang   +3 more
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Optimum Factorization of Non-Negative Matrix Functions

Theory of Probability & Its Applications, 1964
The proposition about optimum factorization of a non-negative matrix function $f(\lambda )$ is generalized for the case where the unknown function $A(z)$ of class $H_2 $ satisfies the inequality \[ A\left( {e^{ - i\lambda } } \right)A^ * \left( {e^{ - i\lambda } } \right) \leqq 2\pi f(\lambda ) \] instead of the usual equality \[ A\left( {e^{ - i ...
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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
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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 Platoš, Petr Gajdoš, Pavel Krömer, Václav Snášel   +3 more
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