Sparse and shift-invariant feature extraction from non-negative data

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. ICASSP 2008 This work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonprofit educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of Mitsubishi Electric Research Laboratories, Inc.; an acknowledgment of the authors and individual contributions to the work; and all applicable portions of the copyright notice. Copying, reproduction, or republishing for any other purpose shall require a license with payment o...

• Type: Conference
• Year: 2008
• Where: ICASSP
• Authors: Paris Smaragdis, Bhiksha Raj, Madhusudana V. S. Shashanka
• Keywords: Electric Research Laboratories, ICASSP 2008, Latent Variable Model, Mitsubishi Electric Research, Signal Processing