@inproceedings{ad6298e3c0fb4b91b2333b66504759ac,
title = "Unsupervised feature selection based on non-parametric mutual information",
abstract = "We present a novel filter approach to unsupervised feature selection based on the mutual information estimation between features. Our feature selection approach does not impose a parametric model on the data and no clustering structure is estimated. Instead, to measure the statistical dependence between features, we employ a mutual information criterion, which is computed by using a non-parametric method, and remove uncorrelated features. Numerical experiments on synthetic and real world tasks show that the performance of our algorithm is comparable to previously suggested state-of-the-art methods.",
keywords = "feature selection, mutual information",
author = "Lev Faivishevsky and Jacob Goldberger",
year = "2012",
doi = "10.1109/mlsp.2012.6349791",
language = "الإنجليزيّة",
isbn = "9781467310260",
series = "IEEE International Workshop on Machine Learning for Signal Processing, MLSP",
booktitle = "2012 IEEE International Workshop on Machine Learning for Signal Processing - Proceedings of MLSP 2012",
note = "2012 22nd IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2012 ; Conference date: 23-09-2012 Through 26-09-2012",
}