A modified minimum classification error (MCE) training algorithm for dimensionality reduction

No Thumbnail Available
File version
Author(s)
Wang, XC
Paliwal, KK
Griffith University Author(s)
Primary Supervisor
Other Supervisors
Editor(s)

S.Y. Kung

Date
2002
Size
File type(s)
Location
License
Abstract

Dimensionality reduction is an important problem in pattern recognition. There is a tendency of using more and more features to improve the performance of classifiers. However, not all the newly added features are helpful to classification. Therefore it is necessary to reduce the dimensionality of feature space for effective and efficient pattern recognition. Two popular methods for dimensionality reduction are Linear Discriminant Analysis (LDA) and Principal Component Analysis (PCA). While these methods are effective, there exists an inconsistency between feature extraction and the classification objective. In this paper we use Minimum Classification Error (MCE) training algorithm for feature dimensionality reduction and classification on Daterding and GLASS databases. The results of MCE training algorithms are compared with those of LDA and PCA.

Journal Title

Journal of VLSI Signal Processing Systems for Signal, Image and Video Technology

Conference Title
Book Title
Edition
Volume

32

Issue
Thesis Type
Degree Program
School
Publisher link
Patent number
Funder(s)
Grant identifier(s)
Rights Statement
Rights Statement
Item Access Status
Note
Access the data
Related item(s)
Subject

Electrical and Electronic Engineering

Persistent link to this record
Citation
Collections