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Direct classification of high-dimensional data in low-dimensional projected feature spaces--comparison of several
R L Somorjai1, B Dolenko, M Mandelzweig
1Institute for Biodiagnostics, National Research Council Canada, 435 Ellice Avenue, Winnipeg, Man., Canada. Ray.Somorjai@nrc-cnrc.gc.ca <Ray.Somorjai@nrc-cnrc.gc.ca>
Journal of Biomedical Informatics
|June 13, 2006
Summary
This study extends the Relative Distance Plane (RDP) mapping for visualizing high-dimensional data to classification tasks. The RDP approach enhances classification accuracy by preserving original distances and enabling novel classifier development for biomedical datasets.
Area of Science:
- Computational biology
- Data visualization
- Machine learning
Background:
- High-dimensional data presents challenges in visualization and classification.
- Existing methods may not fully preserve relative relationships between data points.
- The Relative Distance Plane (RDP) was previously introduced for visualization.
Purpose of the Study:
- To extend the applicability of the RDP mapping from data visualization to classification.
- To develop and evaluate new classification methods utilizing the RDP.
- To improve classification accuracy and confidence for high-dimensional biomedical data.
Main Methods:
- Direct application of RDP mapping to standard classifiers like Linear Discriminant Analysis (LDA) and nearest neighbor methods.
- Development of a novel transvariation probabilities-based classification method within the RDP framework.
- Creation of new coordinate systems by combining reference directions for arbitrary classifier development.
- Implementation of a weighted accuracy approach by cycling through reference pairs to enhance classification confidence.
Main Results:
- Demonstrated successful extension of RDP mapping for classification tasks.
- Achieved increased confidence in classification results through weighted accuracy.
- Evaluated classification performance on multiple high-dimensional biomedical datasets.
- Showcased the versatility of RDP in accommodating various classification algorithms.
Conclusions:
- The RDP mapping is a powerful tool for both visualization and classification of high-dimensional data.
- RDP-based classifiers offer enhanced accuracy and reliability, particularly for complex biomedical datasets.
- The RDP framework provides a flexible platform for developing and integrating diverse classification strategies.