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Scalable Kernel Learning Via The Discriminant Information

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IEEE ICASSP 2020 Virtual Conference May 2020

Kernel approximation methods create explicit, low-dimensional kernel feature maps to deal with the high computational and memory complexity of standard techniques. This work studies a supervised kernel learning methodology to optimize such mappings. We ut

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Scalable Kernel Learning Via The Discriminant Information

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Kernel approximation methods create explicit, low-dimensional kernel feature maps to deal with the high computational and memory complexity of standard techniques. This work studies a supervised kernel learning methodology to optimize such mappings. We ut