Abstract

This paper uses compressive sensing theory to reduce the dimensionality of the correlation matrix estimation in a pattern recognition system. Results show that the correlation matrix can be effectively estimated from compressed measurements using a sparse-based reconstruction algorithm.

© 2018 The Author(s)

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