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There has been continued interest in seeking a theorem describing optimal low-rank approximations to tensors of order 3 or higher… Expand Low-rank matrix approximation is an effective tool in alleviating the memory and computational burdens of kernel methods and… Expand In many applications, the data consist of (or may be naturally formulated as) an $m \times n$ matrix $A$. It is often of interest… Expand We consider the problem of computing low rank approximations of matrices. The novelty of our approach is that the low rank… Expand We consider the problem of approximating a given <i>m</i> × <i>n</i> matrix <b>A</b> by another matrix of specified rank <i>k</i… Expand We study the common problem of approximating a target matrix with a matrix of lower rank. We provide a simple and efficient (EM… Expand This article deals with the solution of integral equations using collocation methods with almost linear complexity. Methods such… Expand This paper concerns the construction of a structured low rank matrix that is nearest to a given matrix. The notion of structured… Expand The singular value decomposition (SVD) has been extensively used in engineering and statistical applications. This method was… Expand Summary. This article considers the problem of approximating a general asymptotically smooth function in two variables, typically… Expand