Depth-Based Novelty Detection and Its Application to Taxonomic Research

It is estimated that less than 10 percent of the world’s species have been described, yet species are being lost daily due to human destruction of natural habitats. The job of describing the earth’s remaining species is exacerbated by the shrinking number of practicing taxonomists and the very slow pace of traditional taxonomic research. In this article, we tackle, from a novelty detection perspective, one of the most important and challenging research objectives in taxonomy – new species identification. We propose a unique and efficient novelty detection framework based on statistical depth functions. Statistical depth functions provide from the “deepest” point a “center-outward ordering” of multidimensional data. In this sense, they can detect observations that appear extreme relative to the rest of the observations, i.e., novelty. Of the various statistical depths, the spatial depth is especially appealing because of its computational efficiency and mathematical tr...

• Type: Conference
• Year: 2007
• Where: ICDM
• Authors: Yixin Chen, Henry L. Bart Jr., Xin Dang, Hanxiang Peng
• Keywords: Data Mining, ICDM 2007, Spatial Depth, Statistical Depth, Statistical Depth Functions