In this paper we study the identifiability of linear switched systems (LSSs ) in discrete-time. The question of identifiability is central to system identification, as it sets the...
We show that Kolmogorov complexity and such its estimators as universal codes (or data compression methods) can be applied for hypothesis testing in a framework of classical mathe...
Sparse representation theory has been increasingly used in the fields of signal processing and machine learning. The standard sparse models are not invariant to spatial transform...
Abstract-- A novel method is proposed for performing multilabel, interactive image segmentation. Given a small number of pixels with user-defined (or pre-defined) labels, one can a...
An implicit assumption of many machine learning algorithms is that all attributes are of the same importance. An algorithm typically selects attributes based solely on their statis...