Sparse Code Shrinkage: Denoising by Nonlinear Maximum Likelihood Estimation
Abstract
Sparse coding is a method for finding a representation of data in which each of the components of the representation is only rarely significantly active. Such a representation is closely related to re(cid:173) dundancy reduction and independent component analysis, and has some neurophysiological plausibility. In this paper, we show how sparse coding can be used for denoising. Using maximum likelihood estimation of nongaussian variables corrupted by gaussian noise, we show how to apply a shrinkage nonlinearity on the components of sparse coding so as to reduce noise. Furthermore, we show how to choose the optimal sparse coding basis for denoising. Our method is closely related to the method of wavelet shrinkage, but has the important benefit over wavelet methods that both the features and the shrinkage parameters are estimated directly from the data.
Cite
Text
Hyvärinen et al. "Sparse Code Shrinkage: Denoising by Nonlinear Maximum Likelihood Estimation." Neural Information Processing Systems, 1998.Markdown
[Hyvärinen et al. "Sparse Code Shrinkage: Denoising by Nonlinear Maximum Likelihood Estimation." Neural Information Processing Systems, 1998.](https://mlanthology.org/neurips/1998/hyvarinen1998neurips-sparse/)BibTeX
@inproceedings{hyvarinen1998neurips-sparse,
title = {{Sparse Code Shrinkage: Denoising by Nonlinear Maximum Likelihood Estimation}},
author = {Hyvärinen, Aapo and Hoyer, Patrik O. and Oja, Erkki},
booktitle = {Neural Information Processing Systems},
year = {1998},
pages = {473-479},
url = {https://mlanthology.org/neurips/1998/hyvarinen1998neurips-sparse/}
}