DocumentCode :
2395284
Title :
L1 regularized projection pursuit for additive model learning
Author :
Zhang, Xiao ; Liang, Lin ; Tang, Xiaoou ; Shum, Heung-Yeung
Author_Institution :
Center for Adv. Study, Tsinghua Univ., Beijing
fYear :
2008
fDate :
23-28 June 2008
Firstpage :
1
Lastpage :
8
Abstract :
In this paper, we present a L1 regularized projection pursuit algorithm for additive model learning. Two new algorithms are developed for regression and classification respectively: sparse projection pursuit regression and sparse Jensen-Shannon Boosting. The introduced L1 regularized projection pursuit encourages sparse solutions, thus our new algorithms are robust to overfitting and present better generalization ability especially in settings with many irrelevant input features and noisy data. To make the optimization with L1 regularization more efficient, we develop an ldquoinformative feature firstrdquo sequential optimization algorithm. Extensive experiments demonstrate the effectiveness of our proposed approach.
Keywords :
learning (artificial intelligence); pattern classification; regression analysis; L1 regularized projection pursuit; additive model learning; classification; informative feature first sequential optimization; regression; sparse Jensen-Shannon boosting; Additives; Asia; Boosting; Cost function; Laplace equations; Neural networks; Pursuit algorithms; Robustness; Unsolicited electronic mail;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on
Conference_Location :
Anchorage, AK
ISSN :
1063-6919
Print_ISBN :
978-1-4244-2242-5
Electronic_ISBN :
1063-6919
Type :
conf
DOI :
10.1109/CVPR.2008.4587356
Filename :
4587356
Link To Document :
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