Results 311 to 320 of about 135,346 (336)

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

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

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

Blind Source Separation on Non-Contact Heartbeat Detection by Non-Negative Matrix Factorization Algorithms

IEEE Transactions on Biomedical Engineering, 2020
In non-contact heart rate (HR) monitoring via Doppler radar, the disturbance from respiration and/or body motion is treated as a key problem on the estimation of HR.
Chen Ye, Kentaroh Toyoda, T. Ohtsuki
semanticscholar   +1 more source

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

Non-negative matrix factorization with α-divergence

Pattern Recognition Letters, 2008
Non-negative matrix factorization (NMF) is a popular technique for pattern recognition, data analysis, and dimensionality reduction, the goal of which is to decompose non-negative data matrix X into a product of basis matrix A and encoding variable matrix S with both A and S allowed to have only non-negative elements. In this paper, we consider Amari's
Cichocki, A, Lee, H, Kim, YD, Choi, S
openaire   +2 more sources

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   +3 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

Image prediction based on non-negative matrix factorization

2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2011
This paper presents a novel spatial texture prediction method based on non-negative matrix factorization. As an extension of template matching, approximation based iterative texture prediction methods have recently been considered for image prediction.
Turkan, Mehmet, Guillemot, Christine
openaire   +3 more sources

Robust multi-view non-negative matrix factorization with adaptive graph and diversity constraints

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