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A kernel-based approach for detecting outliers of high-dimensional biological data
1Department of Computer Science and Engineering, The University of Texas, Arlington, Texas, USA. jung.oh@uta.edu
BMC Bioinformatics
|May 12, 2009
Summary
This study introduces a novel outlier detection method using Kullback-Leibler (KL) divergence for biomedical data. The method effectively identifies outliers in high-dimensional datasets, improving knowledge discovery accuracy.
Area of Science:
- Biomedical data analysis
- Bioinformatics
- Machine learning in biology
Background:
- Biomedical datasets often contain outliers, hindering reliable knowledge discovery.
- Ignoring outliers can lead to inaccurate results and misleading information.
Purpose of the Study:
- To develop a novel outlier detection method for biomedical data.
- To improve the accuracy of knowledge discovery from complex biological datasets.
Main Methods:
- Proposed a new outlier detection method based on Kullback-Leibler (KL) divergence.
- Extended KL divergence for biological sample outlier detection using nearest neighbor sets.
- Addressed non-linearity and singularity issues using feature space mapping and kernel functions.
Main Results:
- The proposed KL divergence method demonstrated superior performance in outlier detection.
- Comparative studies showed better results than Mahalanobis distance and one-class SVM.
- Effective on synthetic, microarray, and mass spectrometry (liver cancer) datasets.
Conclusions:
- The novel KL divergence-based method is effective for outlier detection in biological data.
- Outperforms existing algorithms, especially for small, high-dimensional datasets.
- Enhances the reliability of knowledge discovery in bioinformatics.
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