On the Performance of Chernoff-Distance-Based Linear Dimensionality Reduction Techniques

Autor:	Myriam Herrera, Mohammed L. Ali, Luis Rueda
Rok vydání:	2006
Předmět:	Quadratic equation Dimensionality reduction Statistics Discrete geometry Chernoff distance Linear classifier Quadratic classifier Linear discriminant analysis Classifier (UML) Algorithm Mathematics
Zdroj:	Advances in Artificial Intelligence ISBN: 9783540220046 Canadian Conference on AI
DOI:	10.1007/11766247_40
Popis:	We present a performance analysis of three linear dimensionality reduction techniques: Fisher's discriminant analysis (FDA), and two methods introduced recently based on the Chernoff distance between two distributions, the Loog and Duin (LD) method, which aims to maximize a criterion derived from the Chernoff distance in the original space, and the one introduced by Rueda and Herrera (RH), which aims to maximize the Chernoff distance in the transformed space. A comprehensive performance analysis of these methods combined with two well-known classifiers, linear and quadratic, on synthetic and real-life data shows that LD and RH outperform FDA, specially in the quadratic classifier, which is strongly related to the Chernoff distance in the transformed space. In the case of the linear classifier, the superiority of RH over the other two methods is also demonstrated.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::f7b4b38d31f116581fe2ab2d924d0c39 https://doi.org/10.1007/11766247_40 Zobrazit plný text záznamu