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.

DOI: 10.1109/ICASSP.2008.4518048

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@article{Smaragdis2008SparseAS, title={Sparse and shift-invariant feature extraction from non-negative data}, author={Paris Smaragdis and Bhiksha Raj and Madhusudana V. S. Shashanka}, journal={2008 IEEE International Conference on Acoustics, Speech and Signal Processing}, year={2008}, pages={2069-2072} }