Learning Low-Dimensional Signal Models

Sampling, coding, and streaming even the most essential data, e.g., in medical imaging and weather-monitoring applications, produce a data deluge that severely stresses the avail able analog-to-digital converter, communication bandwidth, and digital-storage resources. Surprisingly, while the ambient data dimension is large in many problems, the relevant information in the data can reside in a much lower dimensional space. This observation has led to several important theoretical and algorithmic developments under different low-dimensional modeling frameworks, such as compressive sensing (CS), matrix completion, and general factor-model representations. These approaches have enabled new measurement systems, tools, and methods for information extraction from dimensionality-reduced or incomplete data. A key aspect of maximizing the potential of such techniques is to develop appropriate data models. In this article, we investigate this challenge from the perspective of nonparametric Bayesian analysis.