Multi-label learning under feature extraction budgets
详细信息 查看全文
- 作者：Pekka Naula ; Antti Airola ; Tapio Salakoski ; Tapio Pahikkala
- 关键词：Feature selection ; Greedy forward selection ; Multi-label learning ; Regularized least-squares
- 刊名：Pattern Recognition Letters
- 出版年：15 April, 2014
- 年：2014
- 卷：40
- 期：Complete
- 页码：56-65
- 全文大小：735 K
文摘
We consider the problem of learning sparse linear models for multi-label prediction tasks under a hard constraint on the number of features. Such budget constraints are important in domains where the acquisition of the feature values is costly. We propose a greedy multi-label regularized least-squares algorithm that solves this problem by combining greedy forward selection search with a cross-validation based selection criterion in order to choose, which features to include in the model. We present a highly efficient algorithm for implementing this procedure with linear time and space complexities. This is achieved through the use of matrix update formulas for speeding up feature addition and cross-validation computations. Experimentally, we demonstrate that the approach allows finding sparse accurate predictors on a wide range of benchmark problems, typically outperforming the multi-task lasso baseline method when the budget is small.