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Series: Stochastics Seminar

Series: Stochastics Seminar

Series: Analysis Seminar

Series: Analysis Seminar

Monday, October 16, 2017 - 14:00 ,
Location: Skiles 005 ,
Dr. Barak Sober ,
Tel Aviv University ,
barakino@gmail.com ,
Organizer: Doron Lubinsky

We approximate a function defined over a *d*-dimensional manifold *M*
⊂*Rn* utilizing only noisy function values at noisy locations on the manifold. To produce
the approximation we do not require any knowledge regarding the manifold
other than its dimension *d*. The approximation scheme is based upon the
Manifold Moving Least-Squares (MMLS) and is therefore resistant to noise in
the domain *M* as well. Furthermore, the approximant is shown to be smooth
and of approximation order of *O(hm+1) *for non-noisy data, where *h* is
the mesh size w.r.t *M,* and *m* is the degree of the local polynomial
approximation. In addition, the proposed algorithm is linear in time with
respect to the ambient space dimension *n*, making it useful for cases
where *d<

Monday, October 16, 2017 - 14:00 ,
Location: Skiles 005 ,
Dr. Barak Sober ,
Tel Aviv University ,
barakino@gmail.com ,
Organizer: Doron Lubinsky
We approximate a function defined over a *d*-dimensional manifold *M*
⊂*Rn* utilizing only noisy function values at noisy locations on the manifold. To produce
the approximation we do not require any knowledge regarding the manifold
other than its dimension *d*. The approximation scheme is based upon the
Manifold Moving Least-Squares (MMLS) and is therefore resistant to noise in
the domain *M* as well. Furthermore, the approximant is shown to be smooth
and of approximation order of *O(hm+1) *for non-noisy data, where *h* is
the mesh size w.r.t *M,* and *m* is the degree of the local polynomial
approximation. In addition, the proposed algorithm is linear in time with
respect to the ambient space dimension *n*, making it useful for cases
where *d<

Series: Geometry Topology Seminar

Series: Geometry Topology Seminar

Series: Analysis Seminar

Series: Analysis Seminar