4 accepted papers
Non-negative matrix factorization (NMF) has found use in fields such as remote sensing and computer vision where the signals of interest are usually non-negative. Data dimensions in these applications can be huge and traditional algorithms break down due to unachievable memory demands. One is then c…
There has been significant interest in sparse inverse covariance estimation in areas such as statistics, machine learning, and signal processing. In this problem, the sparse inverse of a covariance matrix of a multivariate normal distribution is estimated. A Penalised Log-Likelihood (PLL) optimisati…
Significant attention has been given to minimizing a penalized least squares criterion for estimating sparse solutions to large linear systems of equations. The penalty induces sparsity and the natural choice is the so-called l <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://…
High dimensional data is often modeled as a linear combination of a sparse component, a low-rank component, and noise. An example is a video sequence of a busy scene where the background is the low-rank part and the foreground, e.g. moving pedestrians, is the sparse part. Sparse and low rank (SLR) m…