Year: 2012
Type: article
Abstract: We wish to congratulate the authors for their innovative contribution, which is bound to inspire much further research.We find latent variable model selection to be a fantastic application of matrix decomposition methods, namely, the superposition of low-rank and sparse elements.Clearly, the methodology introduced in this paper is of potential interest across many disciplines.In the following, we will first discuss this paper in more detail and then reflect on the versatility of the low-rank + sparse decomposition.
Source: The Annals of Statistics
DOI: 10.1214/12-aos1001
Authors: Emmanuel J. Candés, Mahdi Soltanolkotabi
Institution: Stanford University
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W1891927914