The likelihood-informed subspace (LIS) method offers a viable route to reducing the dimensionality of high-dimensional probability distributions arising in Bayesian inference. LIS identifies an intrinsic low-dimensional linear where target distribution differs most from some tractable reference distribution. Such can be identified using leading eigenvectors Gram matrix gradient log-likelihood f...

‎the global fom and gmres algorithms are among the effective‎ ‎methods to solve sylvester matrix equations‎. ‎in this paper‎, ‎we‎ ‎study these algorithms in the case that the coefficient matrices‎ ‎are real symmetric (real symmetric positive definite) and extract‎ ‎two cg-type algorithms for solving generalized sylvester matrix‎ ‎equations‎. ‎the proposed methods are iterative projection metho...

Subspace clustering assumes that the data is separable into separate subspaces. Such a simple assumption, does not always hold. We assume that, even if raw subspaces, one can learn representation (transform coefficients) such learnt To achieve intended goal, we embed subspace techniques (locally linear manifold clustering, sparse and low rank representation) transform learning. The entire formu...