no code implementations • 9 Mar 2015 • Matthias Seibert, Julian Wörmann, Rémi Gribonval, Martin Kleinsteuber
In many applications, it is also required that the filter responses are obtained in a timely manner, which can be achieved by filters with a separable structure.
no code implementations • 6 Jun 2014 • Matthias Seibert, Julian Wörmann, Rémi Gribonval, Martin Kleinsteuber
The ability of having a sparse representation for a certain class of signals has many applications in data analysis, image processing, and other research fields.
no code implementations • 20 Mar 2014 • Matthias Seibert, Martin Kleinsteuber, Rémi Gribonval, Rodolphe Jenatton, Francis Bach
The main goal of this paper is to provide a sample complexity estimate that controls to what extent the empirical average deviates from the cost function.
no code implementations • 13 Dec 2013 • Rémi Gribonval, Rodolphe Jenatton, Francis Bach, Martin Kleinsteuber, Matthias Seibert
Many modern tools in machine learning and signal processing, such as sparse dictionary learning, principal component analysis (PCA), non-negative matrix factorization (NMF), $K$-means clustering, etc., rely on the factorization of a matrix obtained by concatenating high-dimensional vectors from a training collection.
no code implementations • CVPR 2013 • Simon Hawe, Matthias Seibert, Martin Kleinsteuber
Many techniques in computer vision, machine learning, and statistics rely on the fact that a signal of interest admits a sparse representation over some dictionary.