We treat feature selection and basis selection in a unified framework by introducing the masking matrix. If one considers feature selection as finding a binary mask vector that de...
Abstract--This paper offers a new technique for spatially adaptive estimation. The local likelihood is exploited for nonparametric modeling of observations and estimated signals. T...
Background: The identification of relevant biological features in large and complex datasets is an important step towards gaining insight in the processes underlying the data. Oth...
Yvan Saeys, Sven Degroeve, Dirk Aeyels, Pierre Rou...
We consider the problem of segmentation of images that can be modelled as piecewise continuous signals having unknown, non-stationary statistics. We propose a solution to this pro...
— A novel sparse kernel density estimator is derived based on a regression approach, which selects a very small subset of significant kernels by means of the D-optimality experi...