A Probabilistic Model for Dirty Multi-task Feature Selection

Abstract

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.Daniel Hernández-Lobato gratefully acknowledges the use of the facilities of Centro de Computacin Científica (CCC) at Universidad Autónoma de Madrid. This author also acknowledges financial support from Spanish Plan Nacional I+D+i, Grant TIN2013-42351-P, and from Comunidad de Madrid, Grant S2013/ICE-2845 CASI-CAM-CM. José Miguel Hernández-Lobato acknowledges financial support from the Rafael del Pino Fundation