Mika, Rätsch, Weston, Schölkopf and Müller [Mika, S., Rätsch, G., Weston, J., Schölkopf, B., & Müller, K.-R. (1999). Fisher discriminant analysis with kernels. In Neural networks for signal processing: Vol. IX (pp. 41-48). New York: IEEE Press] introduce a non-linear formulation of Fisher's linear discriminant, based on the now familiar "kernel trick", demonstrating state-of-the-art performance...

The DNA microarray classification technique has gained more popularity in both research and practice. In real data analysis, such as microarray data, the dataset contains a huge number of insignificant and irrelevant features that tend to lose useful information. Classes with high relevance and feature sets with high significance are generally referred for the selected features, which determine...

Title of thesis: A PERFORMANCE CHARACTERIZATION OF KERNEL-BASED ALGORITHMS FOR ANOMALY DETECTION IN HYPERSPECTRAL IMAGERY Hirsh Goldberg Master of Science, 2007 Thesis directed by: Professor Rama Chellappa Department of Electrical Engineering This thesis provides a performance comparison of linear and nonlinear subspacebased anomaly detection algorithms. Using a dual-window technique to separat...

In a variety of applications, kernel machines such as Support Vector Machines (SVMs) have been used with great success often delivering stateof-the-art results. Using the kernel trick, they work on several domains and even enable heterogeneous data fusion by concatenating feature spaces or multiple kernel learning. Unfortunately, they are not suited for truly large-scale applications since they...

of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy TIME SERIES ANALYSIS WITH INFORMATION THEORETIC LEARNING AND KERNEL METHODS By Puskal P. Pokharel December 2007 Chair: Jose C. Principe Major: Electrical and Computer Engineering The major goal of our research is to develop simple and ef...

The kernel RX (KRX) detector proposed by Kwon and Nasrabadi exploits a kernel function to obtain a better detection performance. However, it still has two limits that can be improved. On the one hand, reasonable integration of spatial-spectral information can be used to further improve its detection accuracy. On the other hand, parallel computing can be used to reduce the processing time in ava...

Classification of nonlinear high-dimensional data is usually not amenable to standard pattern recognition techniques because of an underlying nonlinear small sample size conditions. To address the problem, a novel kernel fuzzy dual discriminant analysis learning based on a rank symmetric relation is developed in this paper. First, dual subspaces with rank symmetric relation on the discriminant ...

Real-world image classification, which aims to determine the semantic class of un-labeled images, is a challenging task. In this paper, we focus on two challenges of image classification and propose a method to address both of them simultaneously. The first challenge is that representing images by heterogeneous features, such as color, shape and texture, helps to provide better classification a...