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Communication Dans Un Congrès Année : 2019

On Inner-Product Kernels of High Dimensional Data

Résumé

In this article we investigate the eigenspectrum of inner-product kernel matrices of the type √pK={f(xi T xj/√p)}i,j=1 n . Under a two-class mixture modeling of the input data xi ∈ \mathbbR p , we position ourselves in the regime where the number of data n and their dimension p are both large and comparable, and show, for a wide range of kernel functions f, that the spectrum of K only depends on f via three key parameters, with only two of them useful in extracting the statistical structure from the data. By carefully balancing these two parameters, a significant gain in classification performance is observed on real-world datasets.
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Dates et versions

hal-04417393 , version 1 (25-01-2024)

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Citer

Zhenyu Liao, Romain Couillet. On Inner-Product Kernels of High Dimensional Data. CAMSAP 2019 - IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, Dec 2019, Le Gosier, Guadeloupe, France. pp.579-583, ⟨10.1109/CAMSAP45676.2019.9022455⟩. ⟨hal-04417393⟩
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