Improved outlier detection using sparse coding-based methods

Published on May 1, 2019in Pattern Recognition Letters3.255
· DOI :10.1016/J.PATREC.2019.02.022
Jayanta K. Dutta7
Estimated H-index: 7
Bonny Banerjee11
Estimated H-index: 11
(U of M: University of Memphis)
Abstract Outlier detection is an active area of research in data mining and a large number of algorithms exist. Our goal is to come up with a guideline on how to choose the most appropriate outlier detection algorithm for a given dataset without exploiting any domain- or application-specific information. Extensive experimentations with a number of state-of-the-art algorithms on thousands of benchmark datasets revealed a clear trend. For datasets with low dimensionality and low difficulty level, traditional methods outperform sparse coding-based outlier detection (SCOD) algorithms. But the trend reverses as the dimensionality or difficulty level increases. A threshold emerges as the point of intersection of the trends for SCOD and traditional algorithms, which is 250 and 21 for dimensionality and difficulty level respectively.
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