Learning Subspace Classification Using Subset Approximated Kernel Principal Component Analysis

Phoneme Classification Using Kernel Principal Component Analysis

A substantial number of linear and nonlinear feature space transformation methods have been proposed in recent years. Using the so-called ”kernel-idea” well-known linear techniques such as Principal Component Analysis(PCA), Linear Discriminant Analysis(LDA) and Independent Component Analysis(ICA) can be non-linearized in a general way. The aim of this paper here is twofold. First, we describe t...

Subset Basis Approximation of Kernel Principal Component Analysis

Kernel Principal Component Analysis

A new method for performing a nonlinear form of Principal Component Analysis is proposed. By the use of integral operator kernel functions, one can e ciently compute principal components in high{ dimensional feature spaces, related to input space by some nonlinear map; for instance the space of all possible d{pixel products in images. We give the derivation of the method and present experimenta...

Sparse Structured Principal Component Analysis and Model Learning for Classification and Quality Detection of Rice Grains

In scientific and commercial fields associated with modern agriculture, the categorization of different rice types and determination of its quality is very important. Various image processing algorithms are applied in recent years to detect different agricultural products. The problem of rice classification and quality detection in this paper is presented based on model learning concepts includ...

Gene Expression Data Classification With Kernel Principal Component Analysis

One important feature of the gene expression data is that the number of genes M far exceeds the number of samples N. Standard statistical methods do not work well when N < M. Development of new methodologies or modification of existing methodologies is needed for the analysis of the microarray data. In this paper, we propose a novel analysis procedure for classifying the gene expression data. T...