Stuctured Matrix Estimation and Completion

Olga Klopp (), Yu Lu (), Alexandre Tsybakov () and Harrison H. Zhou ()
Additional contact information
Olga Klopp: ESSEC;CREST
Yu Lu: Yale University
Alexandre Tsybakov: ENSAE;CNRS

No 2017-24, Working Papers from Center for Research in Economics and Statistics

Abstract: We study the problem of matrix estimation and matrix completion under a general framework. This framework includes several important models as special cases such as the gaussian mixture model, mixed membership model, bi-clustering model and dictionary learning. We consider the optimal convergence rates in a minimax sense for estimation of the signal matrix under the Frobenius norm and under the spectral norm. As a consequence of our general result we obtain minimax optimal rates of convergence for various special models. ;Classification-JEL: 62J99, 62H12, 60B20, 15A83

Keywords: matrix completion; matrix estimation; minimax optimality (search for similar items in EconPapers)
Pages: 36 pages
Date: 2017-07-10
New Economics Papers: this item is included in nep-ecm
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://crest.science/RePEc/wpstorage/2017-24.pdf CREST working paper version (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:crs:wpaper:2017-24

Access Statistics for this paper

More papers in Working Papers from Center for Research in Economics and Statistics Contact information at EDIRC.
Bibliographic data for series maintained by Secretariat General () and Murielle Jules Maintainer-Email : murielle.jules@ensae.Fr.