Jelani Nelson (Harvard) - Dimensionality Reduction Via Sparse Matrices
Publication information:
Abstract
Abstract- This talk will discuss sparse Johnson-Lindenstrauss transforms, i.e.sparse linear maps into much lower dimension which preserve the Euclidean geometry of a set of vectors. Both upper and lower bounds will be presented, as well as applications to certain domains such as numerical linear algebra and compressed sensing. Based on various joint works with Jean Bourgain, Daniel M. Kane, and Huy Le Nguyen.
Full text
Abstract- This talk will discuss sparse Johnson-Lindenstrauss transforms, i.e.sparse linear maps into much lower dimension which preserve the Euclidean geometry of a set of vectors. Both upper and lower bounds will be presented, as well as applications to certain domains such as numerical linear algebra and compressed sensing. Based on various joint works with Jean Bourgain, Daniel M. Kane, and Huy Le Nguyen.
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