Matrix approximation is a well-studied area of mathematics, but despite the attention it has received, many open questions remain involving existence, uniqueness, extensions to tensors, and efficient computation. The focus in this talk is on matrix approximation problems constrained in rank, sparsity, and nonnegativity, including a novel approach to uncertainty in matrix entries. Applications to deblurring pictures (images), detecting chemicals, and classifying newswire articles will be discussed. [Go to the full record in the library's catalogue]
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Approximating Matrices: One Picture and One Thousand Words
