Results 271 to 280 of about 23,571 (300)

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 0002
openaire   +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 0001, Yueming Wang 0001   +3 more
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Non-negative Matrix Factorization for Face Recognition

2002
The computer vision problem of face classification under several ambient and unfavorable conditions is considered in this study. Changes in expression, different lighting conditions and occlusions are the relevant factors that are studied in this present contribution.
David Guillamet, Jordi Vitrià
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Non-negative Matrix Factorization with sparse features

2011 IEEE International Conference on Granular Computing, 2011
We propose an approach for Non-negative Matrix Factorization (NMF) with sparseness constraints on feature vectors. It has been believed that the non-negativity constraint in NMF contributes to making the learned features sparse, and some approaches incorporated additional sparseness constraints.
Keigo Kimura, Tetsuya Yoshida
openaire   +1 more source

Quantum Semi Non-negative Matrix Factorization

2021
Semi Non-negative Matrix Factorization (SNMF) is a machine learning algorithm that is used to decompose large data matrices where the data matrix is unconstrained (i.e., it may have mixed signs). We develop the quantum version of SNMF using quantum gradient descent, and we show that the quantum version of SNMF provides an exponential speedup compared ...
Kaoutar Benlamine, Younès Bennani, Basarab Matei, Nistor Grozavu   +3 more
openaire   +1 more source

Correntropy supervised non-negative matrix factorization

2015 International Joint Conference on Neural Networks (IJCNN), 2015
Non-negative matrix factorization (NMF) is a powerful dimension reduction method and has been widely used in many pattern recognition and computer vision problems. However, conventional NMF methods are neither robust enough as their loss functions are sensitive to outliers, nor discriminative because they completely ignore labels in a dataset.
Wenju Zhang, Naiyang Guan, Dacheng Tao, Bin Mao, Xuhui Huang, Zhigang Luo   +5 more
openaire   +1 more source

Non-negative matrix factorization

2017
Non-negative matrix factorization - NMF is a Linear Dimensionality Reduction method, which approximates a high dimensional non-negative data matrix by a multiplica- tion of two low-ranked matrices that preserves the non-negativity of the data. This property has proven to be beneficial as it allows for the approximated data to be interpreted in the same
openaire   +1 more source

Probabilistic Sparse Non-negative Matrix Factorization

2018
In this paper, we propose a probabilistic sparse non-negative matrix factorization model that extends a recently proposed variational Bayesian non-negative matrix factorization model to explicitly account for sparsity. We assess the influence of imposing sparsity within a probabilistic framework on either the loading matrix, score matrix, or both and ...
Jesper Løve Hinrich, Morten Mørup
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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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Incremental Learning in the Non-negative Matrix Factorization

2009
The non-negative matrix factorization (NMF) is capable of factorizing strictly positive data into strictly positive activations and base vectors. In its standard form, the input data must be presented as a batch of data. This means the NMF is only able to represent the input space contained in this batch of data whereas it is not able to adapt to ...
Sven Rebhan, Waqas Sharif, Julian Eggert
openaire   +1 more source