## Imaging and Computing Seminar

### Arthur Szlam , Courant Institute of Mathematical Sciences, NYU

**Title:**

Linear and piecewise linear data analysis

**Abstract:**

Many data sets arising from signal processing or machine learning
problems can be approximately modeled as a union of $K$ low
dimensional linear sets. In this talk I will start by discussing the
case $K=1$, which remains a surprisingly active area of research,
despite more than a hundred years of history and a good understanding
of the mathematics of the problem for many notions of
``approximately'' and ``low''. For larger values of $K$, although
heuristic methods have proved succesful in applications, many basic
mathematical and computational questions remain open. I will talk
about some regimes where we have made progress, and then give some fun
examples in less easy regimes where the math remains murky.