Inside Collection (Course): An Introduction to Compressive Sensing

Summary: This module provides a brief overview of the relationship between model selection, sparse linear regression, and the techniques developed in compressive sensing.

Many of the sparse recovery algorithms we have described so far in this course were originally developed to address the problem of sparse linear regression and model selection in statistics. In this setting we are given some data consisting of a set of input variables and response variables. We will suppose that there are a total of

In linear regression, it is assumed that *model selection* by identifying the most relevant variables in predicting the response.

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