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Group Lasso is an efficient regularized least-square regression algorithm, and is now being used as a computationally feasible method to select grouped variables. In this paper, we address the issue of estimation consistency of the group Lasso with special diagonal matrix. We derive sufficient condition for the consistency of group Lasso under practical assumptions, such as model misspecification...
In the present paper, we treat the problem of learning averages out of a set of symmetric positive-definite matrices (SPDMs). We discuss a possible learning technique based on the differential geometrical properties of the SPDM-manifold which was recently shown to possess a Lie-group structure under appropriate group definition. We first recall some relevant notions from differential geometry, mainly...
In this paper, a novel interacting multiple model (IMM) algorithm is proposed, which utilizes a multi-sensor optimal information fusion rule to combine multiple models in the linear minimum variance sense instead of famous Bayes' rule. Furthermore, the diagonal matrices are used as the updated weights of models, which are applied to distinguish the effects produced by different dimensions of state,...
The problem of distributed estimation in wireless sensor networks (WSNs) has attracted much attention. As bandwidth and power are severely limited in WSNs, one of the main objectives in current WSN research is to design bandwidth and power efficient algorithms. A multitude of studies along this line have appeared. Among them, some previous works (e.g., (A. Ribeiro and G.B. Giannakis, 2006), (Z. Luo,...
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