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- W1770470487 abstract "Kernel-based learning methods revolve around the notion of a kernel or Gram matrix between data points. These square, symmetric, positive semi-definite matrices can informally be regarded as encoding pairwise similarity between all of the objects in a data-set. In this paper we propose an algorithm for manipulating the diagonal entries of a kernel matrix using semi-definite programming. Kernel matrix diagonal dominance reduction attempts to deal with the problem of learning with almost orthogonal features, a phenomenon commonplace in kernel matrices derived from string kernels or Gaussian kernels with small width parameter. We show how this task can be formulated as a semi-definite programming optimization problem that can be solved with readily available optimizers. Theoretically we provide an analysis using Rademacher based bounds to provide an alternative motivation for the 1-norm SVM motivated from kernel diagonal reduction. We assess the performance of the algorithm on standard data sets with encouraging results in terms of approximation and prediction." @default.
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- W1770470487 date "2003-01-01" @default.
- W1770470487 modified "2023-09-27" @default.
- W1770470487 title "Reducing Kernel Matrix Diagonal Dominance Using Semi-definite Programming" @default.
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- W1770470487 doi "https://doi.org/10.1007/978-3-540-45167-9_22" @default.
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