Nec Corporation

Siemens Aktiengesellschaft

North Carolina State University

Xiao Bian

Mariappan S Nadar

Guofei Jiang

Qiu Wang

Hasan Ertan Cetingul

Xia Ning

Hamid Krim

Lucas Plaetevoet

A system, method and computer program product for hierarchical sparse dictionary learning ('HiSDL') to construct a learned dictionary regularized by an a priori over-complete dictionary, includes providing at least one a priori over-complete dictionary for regularization, performing sparse coding of the at least one a priori over-complete dictionary to provide a sparse coded dictionary, using a processor, updating the sparse coded dictionary with regularization using at least one auxiliary variable to provide a learned dictionary, determining whether the learned dictionary converges to an input data set, and outputting the learned dictionary regularized by the at least one a priori over-complete dictionary when the learned dictionary converges to the input data set. The system and method includes, when the learned dictionary lacks convergence, repeating the steps of performing sparse coding, updating the sparse coded dictionary, and determining whether the learned dictionary converges to the input data set.

Hierarchical sparse dictionary learning (HiSDL) for heterogeneous high-dimensional time series

A computer-implemented method of detecting a foreground data in an image sequence using a dual sparse model framework includes creating an image matrix based on a continuous image sequence and initializing three matrices: a background matrix, a foreground matrix, and a coefficient matrix. Next, a subspace recovery process is performed over multiple iterations. This process includes updating the background matrix based on the image matrix and the foreground matrix; minimizing an L&#x2212;1 norm of the coefficient matrix using a first linearized soft-thresholding process; and minimizing an L&#x2212;1 norm of the foreground matrix using a second linearized soft-thresholding process. Then, background images and foreground images are generated based on the background and foreground matrices, respectively.

Robust subspace recovery via dual sparsity pursuit

Growing community of inventors

Raleigh, NC, United States of America

Xiao Bian