Abstract: In the era of data deluge, the development of methods for discovering
structure in high-dimensional data is becoming increasingly important.
Traditional approaches often assume that the data is sampled from a
single low-dimensional manifold. However, in many applications in
signal/image processing, machine learning and computer vision, data in
multiple classes lie in multiple low-dimensional subspaces of a
high-dimensional ambient space. In this talk, I will present methods
from algebraic geometry, sparse representation theory and rank
minimization for clustering and classification of data in multiple
low-dimensional subspaces. I will show how these methods can be
extended to handle noise, outliers as well as missing data. I will also
present applications of these methods to video segmentation and face
clustering.
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