Penghang Yin

Minimization of

We study minimization of the difference of

\ell_{1-2}

\ell_1

for Compressed Sensing

and

Computing Sparse Representation in a Highly Coherent Dictionary Based on Difference of L 1 and L 2

\ell_2

Computations of Optimal Transport Distance with Fisher Information Regularization

norms as a nonconvex and Lipschitz continuous metric for solving constrained and unconstrained compressed sensing problems. We establish exact (stable) sparse recovery results under a restricted isometry property (RIP) condition for the constrained problem, and a full-rank theorem of the sensing matrix restricted to the support of the sparse solution. We present an iterative method for

A geometric blind source separation method based on facet component analysis

\ell_{1-2}

Stochastic Backward Euler: An Implicit Gradient Descent Algorithm for k-Means Clustering

minimization based on the difference of convex functions algorithm and prove that it converges to a stationary point satisfying the first-order optimality condition. We propose a sparsity oriented simulated annealing procedure with non-Gaussian random perturbation and prove the almost sure convergence of the combined algorithm (DCASA) to a global minimum. Computation examples on success rates of sparse solution recovery show that if the sensing matrix is ill-conditioned (non RIP satisfying), then our method is better than existing nonconvex compre...

Linear Feature Transform and Enhancement of Classification on Deep Neural Network

We study analytical and numerical properties of the

l_1

L_1-L_2