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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| Format: | Thesis |
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2008
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| Online Access: | https://hdl.handle.net/10356/3153 |
| _version_ | 1826126381635862528 |
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| author | Qin, Kai |
| author2 | Ponnuthurai N. Suganthan |
| author_facet | Ponnuthurai N. Suganthan Qin, Kai |
| author_sort | Qin, Kai |
| collection | NTU |
| description | 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. |
| first_indexed | 2024-10-01T06:51:46Z |
| format | Thesis |
| id | ntu-10356/3153 |
| institution | Nanyang Technological University |
| last_indexed | 2024-10-01T06:51:46Z |
| publishDate | 2008 |
| record_format | dspace |
| spelling | ntu-10356/31532023-07-04T17:25:41Z Dimensionality and prototype reduction techniques for pattern analysis Qin, Kai Ponnuthurai N. Suganthan School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems DRNTU::Engineering::Computer science and engineering::Computing methodologies::Pattern recognition 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. DOCTOR OF PHILOSOPHY (EEE) 2008-09-17T09:23:25Z 2008-09-17T09:23:25Z 2007 2007 Thesis Qin, K. (2007). Dimensionality and prototype reduction techniques for pattern analysis. Doctoral thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/3153 10.32657/10356/3153 Nanyang Technological University application/pdf |
| spellingShingle | DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems DRNTU::Engineering::Computer science and engineering::Computing methodologies::Pattern recognition Qin, Kai Dimensionality and prototype reduction techniques for pattern analysis |
| title | Dimensionality and prototype reduction techniques for pattern analysis |
| title_full | Dimensionality and prototype reduction techniques for pattern analysis |
| title_fullStr | Dimensionality and prototype reduction techniques for pattern analysis |
| title_full_unstemmed | Dimensionality and prototype reduction techniques for pattern analysis |
| title_short | Dimensionality and prototype reduction techniques for pattern analysis |
| title_sort | dimensionality and prototype reduction techniques for pattern analysis |
| topic | DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems DRNTU::Engineering::Computer science and engineering::Computing methodologies::Pattern recognition |
| url | https://hdl.handle.net/10356/3153 |
| work_keys_str_mv | AT qinkai dimensionalityandprototypereductiontechniquesforpatternanalysis |