TY - GEN
T1 - Supervised low rank matrix approximation for stable feature selection
AU - Alelyani, Salem
AU - Liu, Huan
PY - 2012
Y1 - 2012
N2 - Increasing attention has been focused on the stability of selected features or selection stability, which is becoming a new measure in determining the effectiveness of a feature selection algorithm besides the learning performance. A recent study has shown that data characteristics play a significant role in selection stability. Hence, the solution to selection instability should begin with data. In this work, we propose a novel framework with a noise-reduction step before feature selection. Noise reduction is achieved via well-known low rank matrix approximation techniques (namely SVD and NMF) in a supervised manner to reduce data noise and variance between samples from the same class. The new framework is empirically shown to be highly effective with real high-dimensional datasets improving both selection stability and the precision of selecting relevant features while maintaining the classification accuracy for various feature selection methods.
AB - Increasing attention has been focused on the stability of selected features or selection stability, which is becoming a new measure in determining the effectiveness of a feature selection algorithm besides the learning performance. A recent study has shown that data characteristics play a significant role in selection stability. Hence, the solution to selection instability should begin with data. In this work, we propose a novel framework with a noise-reduction step before feature selection. Noise reduction is achieved via well-known low rank matrix approximation techniques (namely SVD and NMF) in a supervised manner to reduce data noise and variance between samples from the same class. The new framework is empirically shown to be highly effective with real high-dimensional datasets improving both selection stability and the precision of selecting relevant features while maintaining the classification accuracy for various feature selection methods.
KW - Low Rank Approximation
KW - Noise Re-duction
KW - selection algorithms
KW - stability
UR - http://www.scopus.com/inward/record.url?scp=84873583481&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84873583481&partnerID=8YFLogxK
U2 - 10.1109/ICMLA.2012.61
DO - 10.1109/ICMLA.2012.61
M3 - Conference contribution
AN - SCOPUS:84873583481
SN - 9780769549132
T3 - Proceedings - 2012 11th International Conference on Machine Learning and Applications, ICMLA 2012
SP - 324
EP - 329
BT - Proceedings - 2012 11th International Conference on Machine Learning and Applications, ICMLA 2012
T2 - 11th IEEE International Conference on Machine Learning and Applications, ICMLA 2012
Y2 - 12 December 2012 through 15 December 2012
ER -