On the role of ML estimation and Bregman divergences in sparse representation of covariance and precision matrices
This work addresses a domain-specific challenge in signal processing and machine learning for applications like feature descriptors and mixture models, but appears incremental as it builds on existing concepts without claiming major breakthroughs.
The paper tackles the problem of sparse representation for covariance and precision matrices, aiming to maintain signal properties and simplify representation, with a focus on developing efficient formulations and solutions.
Sparse representation of structured signals requires modelling strategies that maintain specific signal properties, in addition to preserving original information content and achieving simpler signal representation. Therefore, the major design challenge is to introduce adequate problem formulations and offer solutions that will efficiently lead to desired representations. In this context, sparse representation of covariance and precision matrices, which appear as feature descriptors or mixture model parameters, respectively, will be in the main focus of this paper.