Sunday, May 28, 14:15-15:15
at 14:00
Schreiber Building, Room 309
The algorithm is based on a novel information theoretic method
for extracting relevant structures from complex data, "the information
bottleneck method". Given any two non-independent random variables
we
propose to compress one of the variables under a constraint on the
mutual
information to the other one. This general principle yields, perhaps
surprisingly, an exact implicit solution which can be obtained via
several
converging algorithms. It also provides a general and rich framework
for
discussing various problems in signal and data analysis and in machine
learning. In this talk I will discuss the application of this principle
to
pairwise data clustering and to joint sequence analysis.
Based partly on joint work with William Bialek and Noam Slonim.
For colloquium schedule, see http://www.math.tau.ac.il/~zwick/colloq.html