Out-of-distribution images are detected by the reconstruction error of a Cosine-Gaussian kernel PCA subspace learned from in-distribution features.
Kernel PCA with the Nystr\"om method
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abstract
The Nystr\"om method is one of the most popular techniques for improving the scalability of kernel methods. However, it has not yet been derived for kernel PCA in line with classical PCA. In this paper we derive kernel PCA with the Nystr\"om method, thereby providing one of the few available options to make kernel PCA scalable. We further study its statistical accuracy through a finite-sample confidence bound on the empirical reconstruction error compared to the full method. The behaviours of the method and bound are illustrated through computer experiments on multiple real-world datasets. As an application of the method we present kernel principal component regression with the Nystr\"om method, as an alternative to Nystr\"om kernel ridge regression for efficient regularized regression with kernels.
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Kernel PCA for Out-of-Distribution Detection: Non-Linear Kernel Selection and Approximation
Out-of-distribution images are detected by the reconstruction error of a Cosine-Gaussian kernel PCA subspace learned from in-distribution features.