Rodolphe Sepulchre, June 2013: Difference between revisions
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{{agenda item|9:30a|Richard Murray, 109 Steele Lab}} | {{agenda item|9:30a|Richard Murray, 109 Steele Lab}} | ||
{{agenda item|9:45a|Meet with Richard's NCS group, 110 Steele}} | {{agenda item|9:45a|Meet with Richard's NCS group, 110 Steele}} | ||
* 9:45-10:45: Necmiye, Mumu, Eric, | * 9:45-10:45: Necmiye, Mumu, Eric, Rangoli | ||
* 10:45-11:45: Enoch, Marcella, Anandh, Dan | * 10:45-11:45: Enoch, Marcella, Anandh, Dan | ||
{{agenda item|11: | {{agenda item|11:45a|Lunch with Venkat, CMS faculty}} | ||
{{agenda item| | {{agenda item|1:15p|Seminar setup}} | ||
{{agenda item|1: | {{agenda item|1:30p|Seminar, 121 ANB}} | ||
{{agenda item|3:00p|Venkat Chandrasekaran, 300 ANB}} | |||
{{agenda item|3:00p| | {{agenda item|3:45p|Lijun Chen, 202 ANB}} | ||
{{agenda item|3:45p| | {{agenda item|4:30p|Done, Meet Caltech car in transportation lot, just East of Steele Lab}} | ||
{{agenda item|4:30p | |||
{{agenda end}} | {{agenda end}} | ||
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fixed-rank matrices to two well-studied manifolds: the Grassmann manifold of linear subspaces and the cone | fixed-rank matrices to two well-studied manifolds: the Grassmann manifold of linear subspaces and the cone | ||
of positive definite matrices. The theory will be illustrated on various applications, including | of positive definite matrices. The theory will be illustrated on various applications, including | ||
low-rank Kalman filtering, linear regression with low-rank priors, matrix completion, and the choice of a suitable | low-rank Kalman filtering, linear regression with low-rank priors, matrix completion, and the choice of a suitable metric for Diffusion Tensor Imaging. | ||
Latest revision as of 16:49, 3 June 2013
Rodolphe Sepulchre will visit Caltech on 3 June 2013 (Mon).
Agenda
| 9:30a | Richard Murray, 109 Steele Lab |
| 9:45a | Meet with Richard's NCS group, 110 Steele
|
| 11:45a | Lunch with Venkat, CMS faculty |
| 1:15p | Seminar setup |
| 1:30p | Seminar, 121 ANB |
| 3:00p | Venkat Chandrasekaran, 300 ANB |
| 3:45p | Lijun Chen, 202 ANB |
| 4:30p | Done, Meet Caltech car in transportation lot, just East of Steele Lab |
Abstract
The geometry of (thin) SVD revisited for large-scale computations
Rodolphe Sepulchre
University of Liege, Belgium
The talk will introduce a riemannian framework for large-scale computations over the set of low-rank matrices. The foundation is geometric and the motivation is algorithmic, with a bias towards efficient computations in large-scale problems. We will explore how classical matrix factorizations connect the riemannian geometry of the set of fixed-rank matrices to two well-studied manifolds: the Grassmann manifold of linear subspaces and the cone of positive definite matrices. The theory will be illustrated on various applications, including low-rank Kalman filtering, linear regression with low-rank priors, matrix completion, and the choice of a suitable metric for Diffusion Tensor Imaging.
