kernel Hilbert spaces (RKHSs). methods, dimensionality reduc- ple, nearly every statistician com-
Interestingly, the use of RKHS tion and graph partitioning mented on the desire for more
ideas to solve this SDR problem methods, and co-clustering and statisticians at the next MMDS;
cannot be viewed as a kerneliza- other matrix factorization meth- nearly every scientific computing
tion of an underlying linear algo- ods, participants heard about a researcher told us they wanted
rithm, as is typically the case when variety of data applications. more data-intensive scientific
such ideas are used (e.g., with In all cases, scalability was a computation at the next MMDS;
SVMs) to provide basis expan- central issue, motivating discus- nearly every practitioner from an
sions for regression and classifica- sion of external memory algo- application domain wanted more
tion. Instead, this is an example rithms, novel computational applications at the next MMDS;
of how RKHS ideas provide algo- paradigms such as MapReduce, and nearly every theoretical com-
rithmically efficient machinery to and communication-efficient lin- puter scientist said they wanted
optimize a much wider range of ear algebra algorithms. Interested more of the same. There is a lot of
statistical functionals of interest. readers are invited to visit http:// interest in MMDs as a develop-
mmds.stanford.edu, where pre- ing interdisciplinary research area
Conclusions and
sentations from all speakers can at the interface between com-
Future Directions be found. puter science, statistics, applied
In addition to other talks on
The feedback received made it mathematics, and scientific and
the theory of data algorithms,
clear that MMDS struck a strong Internet data applications. Keep
machine learning and kernel
interdisciplinary chord. For exam- an eye out for future MMDSs. n
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MacKichan
SOFTWARE, INC.
JUNE 2009 AmstAt News 19
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