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In this paper we address the problem of learning shared sparse representation across several tasks. Assuming that the tasks share a common set of relevant features across all tasks is highly restrictive. This acts as a motivation to look for a generalized model which will be able to learn any correlation structure present between the tasks. We propose a generalized scale mixture distribution, the...
In this paper, we propose a generalized scale mixture family of distributions, namely the Power Exponential Scale Mixture (PESM) family, to model the sparsity inducing priors currently in use for sparse signal recovery (SSR). We show that the successful and popular methods such as LASSO, Reweighted ${\ell }_{1}$ and Reweighted ${\ell }_{2}$ methods can be formulated in an unified manner in a maximum...
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