EigenNet: A Bayesian hybrid of generative and conditional models for sparse learning
作者： Feng Yan , Yuan Qi
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摘要: For many real-world applications, we often need to select correlated variables— such as genetic variations and imaging features associated with Alzheimer's disease—in a high dimensional space. The correlation between variables presents challenge classical variable selection methods. To address this challenge, the elastic net has been developed successfully applied applications. Despite its great success, does not exploit information embedded in data variables. overcome limitation, present novel hybrid model, EigenNet, that uses eigenstructures of guide selection. Specifically, it integrates sparse conditional classification model generative capturing correlations principled Bayesian framework. We develop an efficient active-set algorithm estimate via evidence maximization. Experimental results on synthetic genetics demonstrate superior predictive performance EigenNet over lasso, net, automatic relevance determination.
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arxiv.org参考文章(17)
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