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L1 regularized projection pursuit for additive model learning
| Content Provider | CiteSeerX |
|---|---|
| Author | Zhang, Xiao Liang, Lin Tang, Xiaoou Shum, Heung-Yeung |
| Description | In IEEE CVPR 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 ”informative feature first ” sequential optimization algorithm. Extensive experiments demonstrate the effectiveness of our proposed approach. 1. |
| File Format | |
| Language | English |
| Access Restriction | Open |
| Subject Keyword | Additive Model Sparse Jensen-shannon Boosting Projection Pursuit Sparse Solution Many Irrelevant Input Feature Generalization Ability Sequential Optimization Algorithm Sparse Projection Pursuit Regression New Algorithm Noisy Data Introduced L1 Extensive Experiment Informative Feature Projection Pursuit Algorithm L1 Regularization |
| Content Type | Text |
| Resource Type | Article |
