Feature dimension reduction for microarray data analysis
In this project, we target to find effective and unsupervised feature reduction tools for gene expression data classification purpose. We have tackled the problem from both feature selection and feature extraction approaches. Three feature reduction algo- rithms, fast entropy ranking, revised locall...
محفوظ في:
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| التنسيق: | Theses and Dissertations |
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2008
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| الوصول للمادة أونلاين: | https://hdl.handle.net/10356/3241 |
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| المؤسسة: | Nanyang Technological University |
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sg-ntu-dr.10356-32412023-07-04T17:26:29Z Feature dimension reduction for microarray data analysis Shi, Chao Chen Lihui School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering::Electronic circuits In this project, we target to find effective and unsupervised feature reduction tools for gene expression data classification purpose. We have tackled the problem from both feature selection and feature extraction approaches. Three feature reduction algo- rithms, fast entropy ranking, revised locally linear embedding, and feature grouping are proposed, analyzed and tested on several microarray datasets. MASTER OF ENGINEERING (EEE) 2008-09-17T09:25:21Z 2008-09-17T09:25:21Z 2005 2005 Thesis Shi, C. (2005). Feature dimension reduction for microarray data analysis. Master’s thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/3241 10.32657/10356/3241 Nanyang Technological University application/pdf |
| institution |
Nanyang Technological University |
| building |
NTU Library |
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Asia |
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Singapore Singapore |
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NTU Library |
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DR-NTU |
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DRNTU::Engineering::Electrical and electronic engineering::Electronic circuits |
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DRNTU::Engineering::Electrical and electronic engineering::Electronic circuits Shi, Chao Feature dimension reduction for microarray data analysis |
| description |
In this project, we target to find effective and unsupervised feature reduction tools for gene expression data classification purpose. We have tackled the problem from both feature selection and feature extraction approaches. Three feature reduction algo- rithms, fast entropy ranking, revised locally linear embedding, and feature grouping are proposed, analyzed and tested on several microarray datasets. |
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Chen Lihui |
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Chen Lihui Shi, Chao |
| format |
Theses and Dissertations |
| author |
Shi, Chao |
| author_sort |
Shi, Chao |
| title |
Feature dimension reduction for microarray data analysis |
| title_short |
Feature dimension reduction for microarray data analysis |
| title_full |
Feature dimension reduction for microarray data analysis |
| title_fullStr |
Feature dimension reduction for microarray data analysis |
| title_full_unstemmed |
Feature dimension reduction for microarray data analysis |
| title_sort |
feature dimension reduction for microarray data analysis |
| publishDate |
2008 |
| url |
https://hdl.handle.net/10356/3241 |
| _version_ |
1772829026365210624 |