High Dimensional Data

  • Ravi Kannan | Microsoft Research Lab

Match the applications to the theorems:

  1. Find the variance of traffic volumes in a large network presented as streaming data.
  2. Estimate failure probabilities in a complex systems with many parts.
  3. Group customers into clusters based on what they bought.
  1. Projecting high dimensional space to a random low dimensional space scales each vector’s length by (roughly) the same factor.
  2. A random walk in a high dimensional convex set converges rather fast.
  3. Given data points, we can find their best-fit subspace fast.

While the theorems are precise, the talk will deal with applications at a high level. Other theorems/applications may be discussed.

Speaker Details

Ravindran Kannan is a principal researcher at Microsoft Research India, where he leads the Algorithms Research Group. He is also the first adjunct faculty of the Department of Computer Science and Automation at the Indian Institute of Science.

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