Abstract
Compressive sensing is a technique to acquire signals at rates proportional to the amount of information in the signal, and it does so by exploiting the sparsity of signals. This section discusses the fundamentals of compressive sensing, and how it is related to sparse coding.
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Notes
- 1.
The details about when this would work is presented in later sections.
- 2.
In fact, any distribution satisfying a specific concentration inequality would do (Baraniuk et al. 2008).
- 3.
More information about dictionary training can be found in Sect. 14.7.
- 4.
The magnitude of the signal is lost in this setting.
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Chen, K., Kung, H.T. (2018). Compressive Sensing and Sparse Coding. In: Härdle, W., Lu, HS., Shen, X. (eds) Handbook of Big Data Analytics. Springer Handbooks of Computational Statistics. Springer, Cham. https://doi.org/10.1007/978-3-319-18284-1_14
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