Quadratic polynomial kernel approximation with asymmetric embeddings
(2024) DEEPK24 — Location: Leuven (7.March.2024)
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- Abstract
- Random embedding techniques, such as random Fourier features, are widely used to sketch initial data to a new, kernelised feature space. In this work, we leverage a specific property of random rank-one projection operators, the sign product embedding, to approximate a quadratic polynomial kernel using the scalar product of a pair asymmetric vector embeddings, with one taking only binary values. We demonstrate empirically that the approximated kernel compares favourably to the initial one on toy binary classification examples.
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Delogne, R., & Jacques, L. (2024). Quadratic polynomial kernel approximation with asymmetric embeddings. International Workshop on Deep Learning and Kernel Machines (2024). Published. DEEPK24, Leuven. https://hdl.handle.net/2078.5/252133 (Original work published 2024)