Random Matrices in Data Analysis

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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.

Dimitris Achlioptas

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Accelerate Spectral Computations | Certain Kernel Methods | Distance-preserving Dimensionality Reduction | PKDD 2004 |

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Added	02 Jul 2010
Updated	02 Jul 2010
Type	Conference
Year	2004
Where	PKDD
Authors	Dimitris Achlioptas

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Random Matrices in Data Analysis

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