# An Infinite Dimensional Analysis of Kernel Principal Components

## TL;DR

This paper develops a new kernel PCA framework for nonlinear data dimension reduction, extending classical PCA with manifold and feature space transforms, and provides theoretical insights into optimal kernel choices.

## Contribution

Introduces a novel kernel PCA method with theoretical analysis for optimal Gaussian kernel selection and extends probabilistic Karhunen-Loève transforms to nonlinear settings.

## Key findings

- Proves new theorems for data-dimension reduction.
- Identifies conditions for optimal Gaussian kernel choice.
- Enhances digital image representation and compression.

## Abstract

We study non-linear data-dimension reduction. We are motivated by the classical linear framework of Principal Component Analysis. In nonlinear case, we introduce instead a new kernel-Principal Component Analysis, manifold and feature space transforms. Our results extend earlier work for probabilistic Karhunen-Lo\`eve transforms on compression of wavelet images. Our object is algorithms for optimization, selection of efficient bases, or components, which serve to minimize entropy and error; and hence to improve digital representation of images, and hence of optimal storage, and transmission. We prove several new theorems for data-dimension reduction. Moreover, with the use of frames in Hilbert space, and a new Hilbert-Schmidt analysis, we identify when a choice of Gaussian kernel is optimal.