On the Convergence of Eigenspaces in Kernel Principal Component Analysis

In the multidi- mensional case, making again a logarithmic transformation of the principal eigenfunction, one can reduce the studied spectral problem to an appropriate boundary

It is not a problem for us, since in our final proof of Theorem 1.2, we only need two independent smooth solutions wrt (d, γ 1 , γ 2 ) and having bounded behaviors at 0.. , 4, for

We bring bounds of a new kind for kernel PCA: while our PAC bounds (in Section 3) were assessing that kernel PCA was efficient with a certain amount of confidence, the two

The problems as well as the objective of this realized work are divided in three parts: Extractions of the parameters of wavelets from the ultrasonic echo of the detected defect -

In particu- lar, if the multiple integrals are of the same order and this order is at most 4, we prove that two random variables in the same Wiener chaos either admit a joint

Giroire , Dirichlet and Neumann exterior problems for the n-dimensional Laplace operator: an approach in weighted Sobolev spaces, Journal de Mathématiques Pures et Appliquées, 76

It has been pointed out in a couple of recent works that the weak convergence of its centered and rescaled versions in a weighted Lebesgue L p space, 1 ≤ p < ∞, considered to be

Abstract. Since its introduction, the pointwise asymptotic properties of the kernel estimator f b n of a probability density function f on R d , as well as the asymptotic behaviour