What can you do about data sparsity? What do you do when you have a matrix with a bunch of zeros in it, and you can't get a good look at a complex system because so many of the nodes are empty? Matrix ...

Sparse matrices underpin a vast range of scientific and engineering applications, from finite element analysis and computational fluid dynamics to graph processing and machine learning. By encoding ...

Sparse principal component analysis (SPCA) extends classical principal component analysis to settings where the number of variables greatly exceeds the number of observations. By imposing sparsity ...