  Adaptive sparse coding methods learn a possibly overcomplete set of basis
functions, such that natural image patches can be reconstructed by linearly
combining a small subset of these bases. The applicability of these methods to
visual object recognition tasks has been limited because of the prohibitive
cost of the optimization algorithms required to compute the sparse
representation. In this work we propose a simple and efficient algorithm to
learn basis functions. After training, this model also provides a fast and
smooth approximator to the optimal representation, achieving even better
accuracy than exact sparse coding algorithms on visual object recognition
tasks.
