Multiscale adaptive approximations to data and functions near low-dimensional sets

Series:
Job Candidate Talk
Thursday, January 19, 2017 - 2:00pm
1 hour (actually 50 minutes)
Location:
Skiles 005
,
Johns Hopkins University
,
Organizer:
High-dimensional data arise in many fields of contemporary  science and introduce new challenges in statistical learning due to the  well-known curse of dimensionality. Many data sets in image analysis and  signal processing are in a high-dimensional space but exhibit a  low-dimensional structure. We are interested in building efficient  representations of these data for the purpose of compression and inference,  and giving performance guarantees that are only cursed by the intrinsic  dimension of data. Specifically, in the setting where a data set in $R^D$  consists of samples from a probability measure concentrated on or near an  unknown $d$-dimensional manifold with $d$ much smaller than $D$, we  consider two sets of problems: low-dimensional geometric approximation to  the manifold and regression of a function on the manifold. In the first  case we construct multiscale low-dimensional empirical approximations to the manifold and give finite-sample performance guarantees. In the second   case we exploit these empirical geometric approximations of the manifold to   construct multiscale approximations to the function. We prove finite-sample guarantees showing that we attain the same learning rates as if the function was defined on a Euclidean domain of dimension $d$. In both cases  our approximations can adapt to the regularity of the manifold or the  function even when this varies at different scales or locations. All  algorithms have complexity $C n\log (n)$ where $n$ is the number of  samples, and the constant $C$ is linear in $D$ and exponential in $d$.