Abstract
This chapter presents several applications of SVD. The first one is the pseudoinverse, which plays a crucial role in solving linear systems by the method of least squares. The second application is data compression. The third application is principal component analysis (PCA), whose purpose is to identify patterns in data and understand the variance–covariance structure of the data. The fourth application is the best affine approximation of a set of data, a problem closely related to PCA.
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Gallier, J. (2011). Applications of SVD and Pseudo-inverses. In: Geometric Methods and Applications. Texts in Applied Mathematics, vol 38. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-9961-0_14
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DOI: https://doi.org/10.1007/978-1-4419-9961-0_14
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