Random matrices in data analysis

Authors:
Dimitris Achlioptas
Affiliations:
Microsoft Research, Redmond, WA
Venue:
PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
Year:
2004

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Abstract

We show how carefully crafted random matrices can achieve distance-preserving dimensionality reduction, accelerate spectral computations, and reduce the sample complexity of certain kernel methods.