Improved Nonnegative Matrix Factorization Based Feature Selection for High Dimensional Data Analysis

DOI

10.2991/iccsee.2013.583How to use a DOI?

Keywords

feature selection, nonnegative matrix factorization, reliefF algorithm

Abstract

Feature selection has become the focus of research areas of applications with high dimensional data. Nonnegative matrix factorization (NMF) is a good method for dimensionality reduction but it can’t select the optimal feature subset for it’s a feature extraction method. In this paper, a two-step strategy method based on improved NMF is proposed.The first step is to get the basis of each catagory in the dataset by NMF. Added constrains can guarantee these basises are sparse and mostly distinguish from each other which can contribute to classfication. An auxiliary function is used to prove the algorithm convergent.The classic ReliefF algorithm is used to weight each feature by all the basis vectors and choose the optimal feature subset in the second step.The experimental results revealed that the proposed method can select a representive and relevant feature subset which is effective in improving the performance of the classifier.

Copyright

© 2013, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Lincheng Jiang
AU  - Wentang Tan
AU  - Zhenwen Wang
AU  - Fengjing Yin
AU  - Bin Ge
AU  - Wendong Xiao
PY  - 2013/03
DA  - 2013/03
TI  - Improved Nonnegative Matrix Factorization Based Feature Selection for High Dimensional Data Analysis
BT  - Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering (ICCSEE 2013)
PB  - Atlantis Press
SP  - 2328
EP  - 2331
SN  - 1951-6851
UR  - https://doi.org/10.2991/iccsee.2013.583
DO  - 10.2991/iccsee.2013.583
ID  - Jiang2013/03
ER  -