Please use this identifier to cite or link to this item:
Title: Dimensionality and prototype reduction techniques for pattern analysis
Authors: Qin, Kai
Keywords: DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems
DRNTU::Engineering::Computer science and engineering::Computing methodologies::Pattern recognition
Issue Date: 2007
Source: Qin, K. (2007). Dimensionality and prototype reduction techniques for pattern analysis. Doctoral thesis, Nanyang Technological University, Singapore.
Abstract: 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.
DOI: 10.32657/10356/3153
Rights: Nanyang Technological University
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Theses

Files in This Item:
File Description SizeFormat 
EEE-THESES_1043.pdf1.37 MBAdobe PDFThumbnail

Page view(s) 50

Updated on May 12, 2021

Download(s) 50

Updated on May 12, 2021

Google ScholarTM




Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.