Emerging Trends in Image Processing, Computer Vision and Pattern Recognition

Chapter 36

Distances and kernels based on cumulative distribution functions

Hongjun Su; Hong Zhang    Department of Computer Science and Information Technology, Armstrong State University, Savannah, GA, USA

Abstract

Similarity and dissimilarity measures such as kernels and distances are key components of classification and clustering algorithms. We propose a novel technique to construct distances and kernel functions between probability distributions based on cumulative distribution functions. The proposed distance measures incorporate global discriminating information and can be computed efficiently.

Keywords

Cumulative distribution function

Distance

Kernel

Similarity

1 Introduction

A kernel is a similarity measure that is the key component ...