Correlation Matrix Estimation from Compressed Measurements in a Pattern Recognition System
Descripción
2018/6/25
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.