- P. Smaragdis, B. Raj, M. Shashanka. 2008. Sparse and shift-invariant feature extraction from non-negative data. Abstract: In this paper we describe a technique that allows the extraction of multiple local shift-invariant features from analysis of non-negative data of arbitrary dimensionality. Our approach employs a probabilistic latent variable model with sparsity constraints. We demonstrate its utility by performing feature extraction in a variety of domains ranging from audio to images and video.
