Sparse and shift-invariant feature extraction from non-negative data
Paris Smaragdis, Bhiksha Raj, Madhusudana V. S. Shashanka · IEEE International Conference on Acoustics Speech and Signal Processing · 2008
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.