Missing data recovery using low rank matrix completion methods

Autor:	Ivan Selesnick, Unnikrishna Pillai, Vinay Murthy
Rok vydání:	2012
Předmět:	Mathematical optimization Rank (linear algebra) business.industry Bandwidth (computing) Low-rank approximation business Missing data Convexity Mathematics Sparse matrix Data modeling Data recovery
Zdroj:	2012 IEEE Radar Conference.
DOI:	10.1109/radar.2012.6212119
Popis:	This paper presents a new method for data recovery exploiting low rank structure and convexity constraints. It is capable of handling arbitrary observation patterns and it can incorporate additional convex constraints such as non-negativity and finite bandwidth.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::cba4d2aaeb05e7275016234dec82f377 https://doi.org/10.1109/radar.2012.6212119 Zobrazit plný text záznamu