The development of Multiple Kernel Techniques has become of particular interest for machine learning researchers in Computer Vision topics like image processing, object classification, and object state recognition. Sparsity-inducing norms along with non-sparse formulations promote different degrees of sparsity at the kernel coefficient level, at the same time permitting non-sparse combination w...

MOTIVATION Many practical pattern recognition problems require non-negativity constraints. For example, pixels in digital images and chemical concentrations in bioinformatics are non-negative. Sparse non-negative matrix factorizations (NMFs) are useful when the degree of sparseness in the non-negative basis matrix or the non-negative coefficient matrix in an NMF needs to be controlled in approx...

Sparse coding has been posited as an efficient information processing strategy employed by sensory systems, particularly visual cortex. Substantial theoretical and experimental work has focused on the issue of sparse encoding, namely how the early visual system maps the scene into a sparse representation. In this paper we investigate the complementary issue of sparse decoding, for example given...

Abstract An important element of 3D data-driven simulations solar magnetic fields is the determination horizontal electric field at photosphere. This used to drive and inject energy helicity into corona. One outstanding problem localisation such that it consistent with Ohm’s law. Yeates ( Astrophys. J. 836 (1), 131, 2017) put forward a new “sparse” technique for computing from normal-component ...