Chilean wine varietal classification using quadratic Fisher transformation
Abstract This paper deals with the Chilean red wine varietal classification problem. The problem is solved here by using one of the simplest statistical classification methods based on quadratic discriminant analysis (QDA) together with a new recently introduced nonlinear feature extraction techniqu...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Duarte-Mermoud, Manuel A. [verfasserIn] |
|---|
| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2009 |
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| Schlagwörter: |
Nonlinear Fisher transformation Quadratic Fisher transformation |
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| Anmerkung: |
© Springer-Verlag London Limited 2009 |
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| Übergeordnetes Werk: |
Enthalten in: Pattern analysis and applications - Springer-Verlag, 1998, 13(2009), 2 vom: 25. Feb., Seite 181-188 |
|---|---|
| Übergeordnetes Werk: |
volume:13 ; year:2009 ; number:2 ; day:25 ; month:02 ; pages:181-188 |
| Links: |
|---|
| DOI / URN: |
10.1007/s10044-009-0148-z |
|---|
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OLC2051697752 |
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| 520 | |a Abstract This paper deals with the Chilean red wine varietal classification problem. The problem is solved here by using one of the simplest statistical classification methods based on quadratic discriminant analysis (QDA) together with a new recently introduced nonlinear feature extraction technique called quadratic Fisher transformation. Classification is based on liquid chromatograms of polyphenolic compounds present in wine samples, obtained from a high performance liquid chromatograph with diode alignment detector. For comparison purposes three other feature extraction methods are studied: linear Fisher transformation, Fourier transform and wavelet transform, maintaining QDA as classification scheme. From experimental results it is possible to conclude that when using quadratic discriminant analysis as classification method, the percentage of correct classification was improved from 91% (obtained for the case of wavelet extraction) to 99% when employing quadratic Fisher transformation as feature extraction method. | ||
| 650 | 4 | |a Feature extraction | |
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| 650 | 4 | |a Quadratic feature extraction | |
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| 650 | 4 | |a Quadratic Fisher transformation | |
| 650 | 4 | |a Wavelet transform | |
| 650 | 4 | |a Fourier transform | |
| 650 | 4 | |a Quadratic discriminant analysis | |
| 650 | 4 | |a Fisher discriminant analysis | |
| 700 | 1 | |a Beltrán, Nicolás H. |4 aut | |
| 700 | 1 | |a Bustos, Matías A. |4 aut | |
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Duarte-Mermoud, Manuel A. |
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10.1007/s10044-009-0148-z |
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004 600 |
| title_sort |
chilean wine varietal classification using quadratic fisher transformation |
| title_auth |
Chilean wine varietal classification using quadratic Fisher transformation |
| abstract |
Abstract This paper deals with the Chilean red wine varietal classification problem. The problem is solved here by using one of the simplest statistical classification methods based on quadratic discriminant analysis (QDA) together with a new recently introduced nonlinear feature extraction technique called quadratic Fisher transformation. Classification is based on liquid chromatograms of polyphenolic compounds present in wine samples, obtained from a high performance liquid chromatograph with diode alignment detector. For comparison purposes three other feature extraction methods are studied: linear Fisher transformation, Fourier transform and wavelet transform, maintaining QDA as classification scheme. From experimental results it is possible to conclude that when using quadratic discriminant analysis as classification method, the percentage of correct classification was improved from 91% (obtained for the case of wavelet extraction) to 99% when employing quadratic Fisher transformation as feature extraction method. © Springer-Verlag London Limited 2009 |
| abstractGer |
Abstract This paper deals with the Chilean red wine varietal classification problem. The problem is solved here by using one of the simplest statistical classification methods based on quadratic discriminant analysis (QDA) together with a new recently introduced nonlinear feature extraction technique called quadratic Fisher transformation. Classification is based on liquid chromatograms of polyphenolic compounds present in wine samples, obtained from a high performance liquid chromatograph with diode alignment detector. For comparison purposes three other feature extraction methods are studied: linear Fisher transformation, Fourier transform and wavelet transform, maintaining QDA as classification scheme. From experimental results it is possible to conclude that when using quadratic discriminant analysis as classification method, the percentage of correct classification was improved from 91% (obtained for the case of wavelet extraction) to 99% when employing quadratic Fisher transformation as feature extraction method. © Springer-Verlag London Limited 2009 |
| abstract_unstemmed |
Abstract This paper deals with the Chilean red wine varietal classification problem. The problem is solved here by using one of the simplest statistical classification methods based on quadratic discriminant analysis (QDA) together with a new recently introduced nonlinear feature extraction technique called quadratic Fisher transformation. Classification is based on liquid chromatograms of polyphenolic compounds present in wine samples, obtained from a high performance liquid chromatograph with diode alignment detector. For comparison purposes three other feature extraction methods are studied: linear Fisher transformation, Fourier transform and wavelet transform, maintaining QDA as classification scheme. From experimental results it is possible to conclude that when using quadratic discriminant analysis as classification method, the percentage of correct classification was improved from 91% (obtained for the case of wavelet extraction) to 99% when employing quadratic Fisher transformation as feature extraction method. © Springer-Verlag London Limited 2009 |
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| title_short |
Chilean wine varietal classification using quadratic Fisher transformation |
| url |
https://doi.org/10.1007/s10044-009-0148-z |
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| author2 |
Beltrán, Nicolás H. Bustos, Matías A. |
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Beltrán, Nicolás H. Bustos, Matías A. |
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| up_date |
2024-07-04T05:03:54.794Z |
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