MATH/STATISTICS COFFEE TIME: Singular Value Decomposition
The Department of Mathematics & Statistics will hold its first Coffee Time talk of the semester with Dr. Atilla Sit, who will present "Singular Value Decomposition."
Singular value decomposition (SVD) is one of the most useful methods in linear algebra with many applications in science, engineering, and statistics. It is defined for all matrices—rectangular or square—unlike the more commonly used spectral decomposition in linear algebra. It generalizes the eigendecomposition of a square matrix to any m × n matrix by factorizing it into the product of three matrices, where the one in the middle is a diagonal matrix containing the singular values. We introduce the SVD method and show examples of calculating the SVD by hand or a computer. We also demonstrate applications of SVD to image compression, linear regression, and molecular distance geometry.
Coffee and cookies will be provided.
Event Date: January 31 2:30 p.m. Location: Wallace 344 Cost: Free
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