Kernel Principal Component Analysis

Object Recognition based on Local Steering Kernel and SVM

The proposed method is to recognize objects based on application of Local Steering Kernels (LSK) as Descriptors to the image patches. In order to represent the local properties of the images, patch is to be extracted where the variations occur in an image. To find the interest point, Wavelet based Salient Point detector is used. Local Steering Kernel is then applied to the resultant pixels, in ...

Probabilistic Analysis of Kernel Principal Components

This paper presents a probabilistic analysis of kernel principal components by unifying the theory of probabilistic principal component analysis and kernel principal component analysis. It is shown that, while the kernel component enhances the nonlinear modeling power, the probabilistic structure offers (i) a mixture model for nonlinear data structure containing nonlinear sub-structures, and (i...

Kernel Hebbian Algorithm for Iterative Kernel Principal Component Analysis

A new method for performing a kernel principal component analysis is proposed. By kernelizing the generalized Hebbian algorithm, one can iteratively estimate the principal components in a reproducing kernel Hilbert space with only linear order memory complexity. The derivation of the method and preliminary applications in image hyperresolution are presented. In addition, we discuss the extensio...

Matrix-Based Kernel Subspace Methods

It is a common practice that a matrix, the de facto image representation, is first converted into a vector before fed into subspace analysis or kernel method; however, the conversion ruins the spatial structure of the pixels that defines the image. In this paper, we propose two kernel subspace methods that are directly based on the matrix representation, namely matrix-based kernel principal com...

Subset Basis Approximation of Kernel Principal Component Analysis

New online RKPCA-RN Kernel method Applied to Tennessee Eastman Process

-This paper proposes a new method for online identification of a nonlinear system using RKHS models. The RKHS model is a linear combination of kernel functions applied to the used training set observations. For large datasets, this kernel based to severs computational problems and makes identification techniques unsuitable to the online case. For instance, in the KPCA scheme the Gram matrix ord...