This paper analyzes the performance of the simple thresholding algorithm for sparse signal representations. In particular, in order to be more realistic we introduce a new probabi...
Abstract Thesparsenessoftheencodingofstimulibysingle neurons and by populations of neurons is fundamental to understanding the efficiency and capacity of representations in the br...
Leonardo Franco, Edmund T. Rolls, Nikolaos C. Agge...
We propose a novel, computationally efficient generative topographic model for inferring low dimensional representations of high dimensional data sets, designed to exploit data s...
An image representation framework based on structured sparse model selection is introduced in this work. The corresponding modeling dictionary is comprised of a family of learned ...
We present sparse topical coding (STC), a non-probabilistic formulation of topic models for discovering latent representations of large collections of data. Unlike probabilistic t...