Feature selection based on sparse imputation
Date of Original Version
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. © 2012 IEEE.
Proceedings of the International Joint Conference on Neural Networks
Xu, Jin, Yafeng Yin, Hong Man, and Haibo He. "Feature selection based on sparse imputation." Proceedings of the International Joint Conference on Neural Networks , (2012). doi:10.1109/IJCNN.2012.6252639.