We study the problem of finding the dominant eigenvector of the sample covariance matrix, under additional constraints on the vector: a cardinality constraint limits the number of...
Sparse coding—that is, modelling data vectors as sparse linear combinations of basis elements—is widely used in machine learning, neuroscience, signal processing, and statisti...
Julien Mairal, Francis Bach, Jean Ponce, Guillermo...
In this paper we investigate the usage of random ortho-projections in the compression of sparse feature vectors. The study is carried out by evaluating the compressed features in ...
Wideband analog signals push contemporary analog-to-digital conversion systems to their performance limits. In many applications, however, sampling at the Nyquist rate is inefficie...
Joel A. Tropp, Jason N. Laska, Marco F. Duarte, Ju...
Abstract. An efficient parallel algorithm is presented and tested for computing selected components of H−1 where H has the structure of a Hamiltonian matrix of two-dimensional la...
Lin Lin, Chao Yang, Jianfeng Lu, Lexing Ying, Wein...