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A variational Bayesian framework for group feature selection
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DOI：10.1007/s13042-012-0121-9.png)
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In many machine learning and pattern analysis applications, grouping of features during model development and the selection of a small number of relevant groups can be useful to improve the interpretability of the learned parameters. Although this problem has been receiving a significant amount of attention lately, most of the approaches require the manual tuning of one or more hyper-parameters. In order to overcome this drawback, this work presents a novel hierarchical Bayesian formulation of a generalized linear model and estimates the posterior distribution of the parameters and hyper-parameters of the model within a completely Bayesian paradigm based on variational inference. All the required computations are analytically tractable. The performance and applicability of the proposed framework is demonstrated on synthetic and real world examples.
Keyword:
Feature group selection
Hierarchical bayes
Microarray data analysis
Sensor selection
Variational bayes
Wavelength selection
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