TY - GEN
T1 - Unsupervised feature selection based on non-parametric mutual information
AU - Faivishevsky, Lev
AU - Goldberger, Jacob
PY - 2012
Y1 - 2012
N2 - 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.
AB - 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.
KW - feature selection
KW - mutual information
UR - http://www.scopus.com/inward/record.url?scp=84870688275&partnerID=8YFLogxK
U2 - 10.1109/mlsp.2012.6349791
DO - 10.1109/mlsp.2012.6349791
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AN - SCOPUS:84870688275
SN - 9781467310260
T3 - IEEE International Workshop on Machine Learning for Signal Processing, MLSP
BT - 2012 IEEE International Workshop on Machine Learning for Signal Processing - Proceedings of MLSP 2012
T2 - 2012 22nd IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2012
Y2 - 23 September 2012 through 26 September 2012
ER -