Aug 5, 2008 · Abstract:In multivariate regression, a K-dimensional response vector is regressed upon a common set of p covariates, with a matrix ...

Abstract. We study the behavior of block (cid:96)1/(cid:96)2 regularization for multivariate regression, where a K-dimensional response vector is regressed upon ...

Abstract. In the problem of multivariate regression, a K-dimensional response vector is re- gressed upon a common set of p covariates, with a matrix B∗ ...

Studying this problem under high-dimensional scaling, we show that group Lasso recovers the exact row pattern with high probability over the random design and ...

In multivariate regression, a K-dimensional response vector is regressed upon a common set of p covariates, with a matrix B∗ ∈ Rp×K of regression.

PDF | In the problem of multivariate regression, a K-dimensional response vector is regressed upon a common set of p covariates, with a matrix B* isin.

We study the behavior of block l1/l2 regularization for multivariate regression, where a K-dimensional response vector is regressed upon a fixed set of p co ...

Studying this problem under high-dimensional scaling, we show that group Lasso recovers the exact row pattern with high probability over the random design and ...

Abstract—In the problem of multivariate regression, a K- dimensional response vector is regressed upon a common set of p covariates, with a matrix B∗.

This sparsity-overlap function reveals that, if the design is uncorrelated on the active rows, block lscr1/lscr2 regularization for multivariate regression ...