A Probabilistic Model for Dirty Multi-task Feature Selection


Daniel Hernandez-Lobato, Jose Miguel Hernandez-Lobato, Zoubin Ghahramani ;
Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:1073-1082, 2015.


Multi-task feature selection methods often make the hypothesis that learning tasks share relevant and irrelevant features. However, this hypothesis may be too restrictive in practice. For example, there may be a few tasks with specific relevant and irrelevant features (outlier tasks). Similarly, a few of the features may be relevant for only some of the tasks (outlier features). To account for this, we propose a model for multi-task feature selection based on a robust prior distribution that introduces a set of binary latent variables to identify outlier tasks and outlier features. Expectation propagation can be used for efficient approximate inference under the proposed prior. Several experiments show that a model based on the new robust prior provides better predictive performance than other benchmark methods.

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