TY - JOUR
T1 - Joint embedding learning and sparse regression
T2 - A framework for unsupervised feature selection
AU - Hou, Chenping
AU - Nie, Feiping
AU - Li, Xuelong
AU - Yi, Dongyun
AU - Wu, Yi
PY - 2014/6
Y1 - 2014/6
N2 - Feature selection has aroused considerable research interests during the last few decades. Traditional learning-based feature selection methods separate embedding learning and feature ranking. In this paper, we propose a novel unsupervised feature selection framework, termed as the joint embedding learning and sparse regression (JELSR), in which the embedding learning and sparse regression are jointly performed. Specifically, the proposed JELSR joins embedding learning with sparse regression to perform feature selection. To show the effectiveness of the proposed framework, we also provide a method using the weight via local linear approximation and adding the ℓ2,1-norm regularization, and design an effective algorithm to solve the corresponding optimization problem. Furthermore, we also conduct some insightful discussion on the proposed feature selection approach, including the convergence analysis, computational complexity, and parameter determination. In all, the proposed framework not only provides a new perspective to view traditional methods but also evokes some other deep researches for feature selection. Compared with traditional unsupervised feature selection methods, our approach could integrate the merits of embedding learning and sparse regression. Promising experimental results on different kinds of data sets, including image, voice data and biological data, have validated the effectiveness of our proposed algorithm.
AB - Feature selection has aroused considerable research interests during the last few decades. Traditional learning-based feature selection methods separate embedding learning and feature ranking. In this paper, we propose a novel unsupervised feature selection framework, termed as the joint embedding learning and sparse regression (JELSR), in which the embedding learning and sparse regression are jointly performed. Specifically, the proposed JELSR joins embedding learning with sparse regression to perform feature selection. To show the effectiveness of the proposed framework, we also provide a method using the weight via local linear approximation and adding the ℓ2,1-norm regularization, and design an effective algorithm to solve the corresponding optimization problem. Furthermore, we also conduct some insightful discussion on the proposed feature selection approach, including the convergence analysis, computational complexity, and parameter determination. In all, the proposed framework not only provides a new perspective to view traditional methods but also evokes some other deep researches for feature selection. Compared with traditional unsupervised feature selection methods, our approach could integrate the merits of embedding learning and sparse regression. Promising experimental results on different kinds of data sets, including image, voice data and biological data, have validated the effectiveness of our proposed algorithm.
KW - Embedding learning
KW - feature selection
KW - pattern recognition
KW - sparse regression
UR - http://www.scopus.com/inward/record.url?scp=84901250680&partnerID=8YFLogxK
U2 - 10.1109/TCYB.2013.2272642
DO - 10.1109/TCYB.2013.2272642
M3 - 文章
C2 - 23893760
AN - SCOPUS:84901250680
SN - 2168-2267
VL - 44
SP - 793
EP - 804
JO - IEEE Transactions on Cybernetics
JF - IEEE Transactions on Cybernetics
IS - 6
M1 - 6565365
ER -