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Singular Value Decomposition (SVD) is a matrix decomposition technique developed during the 18th century and has been in use ever since. SVD has applications in several areas including image processing, natural language processing (NLP), genomics, and data compression. In NLP context, SVD is called latent semantic indexing (LSI) and used for concept based search and topic modeling. In this talk, we will describe the math and intuition behind eigenvalues, eigenvectors and their relation to SVD. We will also discuss specific applications of SVD in image processing and NLP with examples.
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