Corpus ID: 44256026

THE SINGULAR VALUE DECOMPOSITION AND LOW RANK APPROXIMATION

@inproceedings{Mazeika2016THESV,
  title={THE SINGULAR VALUE DECOMPOSITION AND LOW RANK APPROXIMATION},
  author={Mantas Mazeika},
  year={2016}
}
The purpose of this paper is to present a largely self-contained proof of the singular value decomposition (SVD), and to explore its application to the low rank approximation problem. We begin by proving background concepts used throughout the paper. We then develop the SVD by way of the polar decomposition. Finally, we show that the SVD can be used to achieve the best low rank approximation of a matrix with respect to a large family of norms. 

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Singular Value Decomposition (SVD) and Polar Form
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