Abstract
The goal of this chapter is to demonstrate how concentration of measure plays a central role in these modern randomized algorithms. There is a convergence of sensing, computing, networking and control. Data base is often neglected in traditional treatments in estimation, detection, etc.
Modern scientific computing demands efficient algorithms for dealing with large datasets—Big Data. Often these datasets can be fruitfully represented and manipulated as matrices; in this case, fast low-error methods for making basic linear algebra computations are key to efficient algorithms. Examples of such foundational computational tools are low-rank approximations, matrix sparsification, and randomized column subset selection.
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Notes
- 1.
Outer products x y T of two vectors x and y are rank-one matrices.
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Qiu, R., Wicks, M. (2014). Database Friendly Data Processing. In: Cognitive Networked Sensing and Big Data. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-4544-9_12
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DOI: https://doi.org/10.1007/978-1-4614-4544-9_12
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