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Multi-Label Learning Under Feature Extraction Budgets

Pekka Naula, Antti Airola, Tapio Salakoski, Tapio Pahikkala, Multi-Label Learning Under Feature Extraction Budgets. Pattern Recognition Letters 40, 56–65, 2014.

http://dx.doi.org/10.1016/j.patrec.2013.12.009

Abstract:

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.

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BibTeX entry:

@ARTICLE{jNaAiSaPa14a,
  title = {Multi-Label Learning Under Feature Extraction Budgets},
  author = {Naula, Pekka and Airola, Antti and Salakoski, Tapio and Pahikkala, Tapio},
  journal = {Pattern Recognition Letters},
  volume = {40},
  pages = {56–65},
  year = {2014},
  keywords = {Feature selection, Greedy forward selection, Multi-label learning, Regularized least-squares},
  ISSN = {0167-8655},
}

Belongs to TUCS Research Unit(s): Algorithmics and Computational Intelligence Group (ACI)

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