Sparse representation classification via sequential Lasso screening

The sparse representation of signals with respect to an over-complete dictionary has been of recent interest in a broad range of applications. One of the most used methods for obtaining sparse codes, the Lasso problem, becomes computationally costly for large dictionaries and this hinders the use of this approach to large-scale decision tasks. Recently, dictionary screening has been used to address this computational issue. In this spirit, we show how sequential Lasso screening can also facilitate faster completion of sparse representation decision tasks, such as classification, without affecting statistical accuracy. Moreover, the sequential screening process allows us to employ an early decision mechanism that can further accelerate classification, possibly at the cost of small decrease in accuracy.We demonstrate this empirically for several classification tasks. In particular, for clip-level music genre classification, using scattering features and a new voting scheme, we show that the proposed method yields improved clip classification accuracy and considerable computational speedup.