Arbeitspapier
PLS dimension reduction for classification of microarray data
PLS dimension reduction is known to give good prediction accuracy in the context of classification with high-dimensional microarray data. In this paper, PLS is compared with some of the best state-of-the-art classification methods. In addition, a simple procedure to choose the number of components is suggested. The connection between PLS dimension reduction and gene selection is examined and a property of the first PLS component for binary classification is proven. PLS can also be used as a visualization tool for high-dimensional data in the classification framework. The whole study is based on 9 real microarray cancer data sets.
- Language
-
Englisch
- Bibliographic citation
-
Series: Discussion Paper ; No. 392
- Event
-
Geistige Schöpfung
- (who)
-
Boulesteix, Anne-Laure
- Event
-
Veröffentlichung
- (who)
-
Ludwig-Maximilians-Universität München, Sonderforschungsbereich 386 - Statistische Analyse diskreter Strukturen
- (where)
-
München
- (when)
-
2004
- DOI
-
doi:10.5282/ubm/epub.1762
- Handle
- URN
-
urn:nbn:de:bvb:19-epub-1762-7
- Last update
-
10.03.2025, 11:44 AM CET
Data provider
This object is provided by:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Arbeitspapier
Associated
- Boulesteix, Anne-Laure
- Ludwig-Maximilians-Universität München, Sonderforschungsbereich 386 - Statistische Analyse diskreter Strukturen
Time of origin
- 2004
Other Objects (12)
Dimension reduction and classification with high-dimensional microarray data
Dimension reduction and classification with high-dimensional microarray data
Sequential dimension reduction and prediction methods with high-dimensional microarray data
Dimension Reduction Methods
Identification of interaction patterns and classification with applications to microarray data
Robust Estimation of Dimension Reduction Space
Dynamic dimension reduction for financial applications
Optimal vs. Classical Linear Dimension Reduction
Optimal vs. classical linear dimension reduction
Robust estimation of dimension reduction space
Dynamic dimension reduction in ODE models
PLS
Dimension reduction and classification with high-dimensional microarray data
Dimension reduction and classification with high-dimensional microarray data
Sequential dimension reduction and prediction methods with high-dimensional microarray data
Dimension Reduction Methods
Identification of interaction patterns and classification with applications to microarray data
Robust Estimation of Dimension Reduction Space
Dynamic dimension reduction for financial applications
Optimal vs. Classical Linear Dimension Reduction
Optimal vs. classical linear dimension reduction
Robust estimation of dimension reduction space
Dynamic dimension reduction in ODE models
PLS
Dimension reduction and classification with high-dimensional microarray data
Dimension reduction and classification with high-dimensional microarray data
Sequential dimension reduction and prediction methods with high-dimensional microarray data
Dimension Reduction Methods
Identification of interaction patterns and classification with applications to microarray data
Robust Estimation of Dimension Reduction Space
Dynamic dimension reduction for financial applications
Optimal vs. Classical Linear Dimension Reduction
Optimal vs. classical linear dimension reduction
Robust estimation of dimension reduction space
Dynamic dimension reduction in ODE models