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There is now a CONTENT FREEZE for Mercury while we switch to a new platform. It began on Friday, March 10 at 6pm and will end on Wednesday, March 15 at noon. No new content can be created during this time, but all material in the system as of the beginning of the freeze will be migrated to the new platform, including users and groups. Functionally the new site is identical to the old one. webteam@gatech.edu
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The Joy of PCA
Santosh Vempala (Georgia Tech)
Principal Component Analysis is the most widely used technique for high-dimensional or large data. For typical applications (nearest neighbor, clustering, learning), it is not hard to build examples on which PCA *fails*. Yet, it is popular and successful across a variety of data-rich areas. In this talk, we focus on two algorithmic problems where the performance of PCA is provably near-optimal, and no other method is known to have similar guarantees. The problems we consider are (a) the classical statistical problem of unraveling a sample from a mixture of k unknown Gaussians and (b) the classic learning theory problem of learning an intersection of k halfspaces. During the talk, we will encounter recent extensions of PCA that are noise-resistant, affine-invariant and nonviolent.
