Dimensionality and prototype reduction techniques for pattern analysis

Dimensionality and prototype reduction techniques for pattern analysis

This thesis investigates two important topics in the statistical pattern recognition field, namely dimensionality reduction for supervised classification and prototype reduction for unsupervised classification. For dimensionality reduction part, we concentrate on the Discriminative Linear Dimensiona...

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Bibliographic Details
Main Author: Qin, Kai
Other Authors: Ponnuthurai N. Suganthan
Format: Theses and Dissertations
Published: 2008
Subjects:
DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems
DRNTU::Engineering::Computer science and engineering::Computing methodologies::Pattern recognition
Online Access:https://hdl.handle.net/10356/3153
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Institution: Nanyang Technological University
Description
Summary:This thesis investigates two important topics in the statistical pattern recognition field, namely dimensionality reduction for supervised classification and prototype reduction for unsupervised classification. For dimensionality reduction part, we concentrate on the Discriminative Linear Dimensionality Reduction (DLDR) techniques with feature extraction for supervised classification as the major application. For prototype reduction part, we focus on the prototype-based clustering algorithms.

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