TY - GEN
T1 - Feature selection based on sparse imputation
AU - Xu, Jin
AU - Yin, Yafeng
AU - Man, Hong
AU - He, Haibo
PY - 2012
Y1 - 2012
N2 - Feature selection, which aims to obtain valuable feature subsets, has been an active topic for years. How to design an evaluating metric is the key for feature selection. In this paper, we address this problem using imputation quality to search for the meaningful features and propose feature selection via sparse imputation (FSSI) method. The key idea is utilizing sparse representation criterion to test individual feature. The feature based classification is used to evaluate the proposed method. Comparative studies are conducted with classic feature selection methods (such as Fisher score and Laplacian score). Experimental results on benchmark data sets demonstrate the effectiveness of FSSI method.
AB - Feature selection, which aims to obtain valuable feature subsets, has been an active topic for years. How to design an evaluating metric is the key for feature selection. In this paper, we address this problem using imputation quality to search for the meaningful features and propose feature selection via sparse imputation (FSSI) method. The key idea is utilizing sparse representation criterion to test individual feature. The feature based classification is used to evaluate the proposed method. Comparative studies are conducted with classic feature selection methods (such as Fisher score and Laplacian score). Experimental results on benchmark data sets demonstrate the effectiveness of FSSI method.
UR - http://www.scopus.com/inward/record.url?scp=84865101778&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84865101778&partnerID=8YFLogxK
U2 - 10.1109/IJCNN.2012.6252639
DO - 10.1109/IJCNN.2012.6252639
M3 - Conference contribution
AN - SCOPUS:84865101778
SN - 9781467314909
T3 - Proceedings of the International Joint Conference on Neural Networks
BT - 2012 International Joint Conference on Neural Networks, IJCNN 2012
T2 - 2012 Annual International Joint Conference on Neural Networks, IJCNN 2012, Part of the 2012 IEEE World Congress on Computational Intelligence, WCCI 2012
Y2 - 10 June 2012 through 15 June 2012
ER -