Online Adaptive Statistical Compressed Sensing of Gaussian Mixture Models

Abstract

A framework of online adaptive statistical compressed sensing is introduced for signals following a mixture model. The scheme first uses non-adaptive measurements, from which an online decoding scheme estimates the model selection. As soon as a candidate model has been selected, an optimal sensing scheme for the selected model continues to apply. The final signal reconstruction is calculated from the ensemble of both the non-adaptive and the adaptive measurements. For signals generated from a Gaussian mixture model, the online adaptive sensing algorithm is given and its performance is analyzed. On both synthetic and real image data, the proposed adaptive scheme considerably reduces the average reconstruction error with respect to standard statistical compressed sensing that uses fully random measurements, at a marginally increased computational complexity.

References in corpus (1)

• Statistical Compressed Sensing of Gaussian Mixture Models

Cited by in corpus (4)

• Task-Driven Adaptive Statistical Compressive Sensing of Gaussian Mixture Models
• Reconstruction of Signals Drawn from a Gaussian Mixture from Noisy Compressive Measurements
• Bounds on the Number of Measurements for Reliable Compressive Classification
• Compressive Classification of a Mixture of Gaussians: Analysis, Designs and Geometrical Interpretation