Model-based Sparse Coding beyond Gaussian Independent Model

TL;DR

This paper introduces a flexible model-based sparse coding framework capable of handling various data types and correlations, with a fast EM algorithm demonstrating superior performance in diverse real-world applications.

Contribution

It presents a novel MSC method that extends sparse coding beyond Gaussian models, accommodating different data types and correlation structures, with scalable estimation techniques.

Findings

Effective in image denoising

Useful for brain connectivity analysis

Applicable to spatial transcriptomic imaging

Abstract

Sparse coding aims to model data vectors as sparse linear combinations of basis elements, but a majority of related studies are restricted to continuous data without spatial or temporal structure. A new model-based sparse coding (MSC) method is proposed to provide an effective and flexible framework for learning features from different data types: continuous, discrete, or categorical, and modeling different types of correlations: spatial or temporal. The specification of the sparsity level and how to adapt the estimation method to large-scale studies are also addressed. A fast EM algorithm is proposed for estimation, and its superior performance is demonstrated in simulation and multiple real applications such as image denoising, brain connectivity study, and spatial transcriptomic imaging.