TY - GEN
T1 - L0-Constrained Regression for Data Mining
AU - Wu, Zhili
AU - Li, Chun-hung
N1 - Publisher copyright:
© Springer-Verlag Berlin Heidelberg 2007
PY - 2007/4/27
Y1 - 2007/4/27
N2 - L2 and L1 constrained regression methods, such as ridge regression and
Lasso, have been generally known for their fitting ability. Recently,
L0-constrained classifications have been used for feature selection and
classifier construction. This paper proposes an L0 constrained
regression method, which aims to minimize both the epsilon-insensitive
fitting errors and L0 constraints on regression coefficients. Our
L0-constrained regression can be efficiently approximated by successive
linearization algorithm, and shows the favorable properties of selecting
a compact set of fitting coefficients and tolerating small fitting
errors. To make our L0 constrained regression generally applicable, the
extension to nonlinear regression is also addressed in this paper.
AB - L2 and L1 constrained regression methods, such as ridge regression and
Lasso, have been generally known for their fitting ability. Recently,
L0-constrained classifications have been used for feature selection and
classifier construction. This paper proposes an L0 constrained
regression method, which aims to minimize both the epsilon-insensitive
fitting errors and L0 constraints on regression coefficients. Our
L0-constrained regression can be efficiently approximated by successive
linearization algorithm, and shows the favorable properties of selecting
a compact set of fitting coefficients and tolerating small fitting
errors. To make our L0 constrained regression generally applicable, the
extension to nonlinear regression is also addressed in this paper.
KW - Feature Selection
KW - Ordinary Little Square
KW - Nonlinear Regression
KW - Support Vector Regression
KW - Ridge Regression
UR - http://www.scopus.com/inward/record.url?scp=38049175261&partnerID=8YFLogxK
U2 - 10.1007/978-3-540-71701-0_110
DO - 10.1007/978-3-540-71701-0_110
M3 - Conference proceeding
AN - SCOPUS:38049175261
SN - 9783540717003
SN - 3540717005
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 981
EP - 988
BT - Advances in Knowledge Discovery and Data Mining - 11th Pacific-Asia Conference, PAKDD 2007, Proceedings
A2 - Zhou, Zhi-Hua
A2 - Li, Hang
A2 - Yang, Qiang
PB - Springer Verlag
T2 - 11th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2007
Y2 - 22 May 2007 through 25 May 2007
ER -