期刊
JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS
卷 28, 期 1, 页码 36-47出版社
TAYLOR & FRANCIS INC
DOI: 10.1080/10618600.2018.1482763
关键词
Convex optimization; Degrees of freedom; Hutchinson estimator; Information criterion; Penalization; Sparse linear systems
资金
- NCSU Faculty Research and Professional Development (FRPD) program
- NIH [R01 CA214955]
- CPRIT [RP150578, RP170719]
- American Cancer Society [RSG-16-005-01]
We consider the problem of performing matrix completion with side information on row-by-row and column-by-column similarities. We build upon recent proposals for matrix estimation with smoothness constraints with respect to row and column graphs. We present a novel iterative procedure for directly minimizing an information criterion to select an appropriate amount of row and column smoothing, namely, to perform model selection. We also discuss how to exploit the special structure of the problem to scale up the estimation and model selection procedure via the Hutchinson estimator, combined with a stochastic Quasi-Newton approach. Supplementary material for this article is available online.
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