NDLI: Biological Data Outlier Detection Based on Kullback-Leibler Divergence

Content Provider	IEEE Xplore Digital Library
Author	Jung Hun Oh Jean Gao Rosenblatt, K.
Copyright Year	2008
Description	Author affiliation: Dept. of Biochem. & Mol. Biol., Univ. of Texas Med. Branch, Galveston, TX (Rosenblatt, K.) \|\| Dept. of Comput. Sci. & Eng., Univ. of Texas, Arlington, TX (Jung Hun Oh; Jean Gao)
Abstract	Outlier detection is imperative in biomedical data analysis to achieve reliable knowledge discovery. In this paper, a new outlier detection method based on Kullback-Leibler (KL) divergence is presented. The original concept of KL divergence was designed as a measure of distance between two distributions. Stemming from that, we extend it to biological sample outlier detection by forming sample sets composed of nearest neighbors. To handle the non-linearity during the KL divergence calculation and to tackle with the singularity problem due to small sample size, we map the original data into a higher feature space and apply kernel functions without resorting to a mapping function. A sample possessing the largest KL divergence is detected as an outlier. The proposed method is tested with one synthetic data, two public gene expression data sets, and our own mass spectrometry data generated for prostate cancer study.
Starting Page	249
Ending Page	254
File Size	169388
Page Count	6
File Format	PDF
ISBN	9780769534527
DOI	10.1109/BIBM.2008.76
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-11-03
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Data analysis outllier detection Intrusion detection Clustering algorithms Object detection mass spectrometry Biology Kernel Bioinformatics Medical diagnostic imaging Nearest neighbor searches
Content Type	Text
Resource Type	Article

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