Compute Less to Get More: Using ORC to Improve Sparse Filtering

Sparse Filtering is a popular feature learning algorithm for image classification pipelines. In this paper, we connect the performance of Sparse Filtering in image classification pipelines to spectral properties of the corresponding feature matrices. This connection provides new insights into Sparse Filtering; in particular, it suggests stopping Sparse Filtering early. We therefore introduce the Optimal Roundness Criterion (ORC), a novel stopping criterion for Sparse Filtering. We show that this stopping criterion is related with pre-processing procedures such as Statistical Whitening and that it can make image classification with Sparse Filtering considerably faster and more accurate.