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In this paper, we present the optimization formulation of the Kalman filtering and smoothing problems, and use this perspective to develop a variety of extensions and applications. We first formulate ...
We consider estimation in a high-dimensional linear model with strongly correlated variables. We propose to cluster the variables first and do subsequent sparse estimation such as the Lasso for cluste...
This paper examines the ability of greedy algorithms to estimate a block sparse parameter vector from noisy measurements.In particular, block sparse versions of the orthogonal matching pursuit and th...
We consider the least-square linear regression problem with regularization by the ℓ1-norm, a problem usually referred to as the Lasso. In this paper, we first present a detailed asymptotic ana...

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