- Working Paper
It is natural to ask: what kinds of matrices satisfy the Restricted Eigenvalue (RE) condition? In this paper, we associate the RE condition (Bickel-Ritov-Tsybakov 09) with the complexity of a subset of the sphere in Rp, where p is the dimensionality of the data, and show that a class of random matrices with independent rows, but not necessarily independent columns, satisfy the RE condition, when the sample size is above a certain lower bound. Here we explicitly introduce an additional covariance structure to the class of random matrices that we have known by now that satisfy the Restricted Isometry Property as defined in Cand`es and Tao 05 (and hence the RE condition), in order to compose a broader class of random matrices for which the RE condition holds. In this case, tools from geometric functional analysis in characterizing the intrinsic low-dimensional structures associated with the RE condition has been crucial in analyzing the sample complexity and understanding its statistical implications for high dimensional data Show more
Pages / Article No.
SubjectHigh dimensional data; Statistical estimation; l1 minimization; Sparsity; Lasso; Dantzig selector; Restricted Isometry Property; Restricted Eigenvalue conditions; Subgaussian random matrices
Organisational unit02537 - Seminar für Statistik (SfS) / Seminar for Statistics (SfS)
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