Fixed Point Iteration Algorithms for Low-rank Matrix Completion
dc.contributor.author | Huang, Xingliang | |
dc.date.accessioned | 2015-05-20 15:29:45 (GMT) | |
dc.date.available | 2015-05-20 15:29:45 (GMT) | |
dc.date.issued | 2015-05-20 | |
dc.date.submitted | 2015 | |
dc.identifier.uri | http://hdl.handle.net/10012/9370 | |
dc.description.abstract | A lot of applications can be formulated as matrix completion problems. In order to address such problems, a common assumption is that the underlying matrix is (approximately) low-rank. Under certain conditions, the recovery of low-rank matrix can be done via nuclear norm minimization, a convex program. Scalable and fast algorithms are essential as the practical matrix completion tasks always occur on a large scale. Here we study two algorithms and generalize the uni ed framework of xed point iteration algorithm. We derive the convergence results and propose a new algorithm based on the insights. Compared with the baseline algorithms, our proposed method is signi cantly more e cient without loss of precision and acceleration potentiality. iii | en |
dc.language.iso | en | en |
dc.publisher | University of Waterloo | en |
dc.title | Fixed Point Iteration Algorithms for Low-rank Matrix Completion | en |
dc.type | Master Thesis | en |
dc.pending | false | |
dc.subject.program | Statistics | en |
uws-etd.degree.department | Statistics and Actuarial Science | en |
uws-etd.degree | Master of Mathematics | en |
uws.typeOfResource | Text | en |
uws.peerReviewStatus | Unreviewed | en |
uws.scholarLevel | Graduate | en |