RODS: Rarity based Outlier Detection in a Sparse Coding Framework
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RODS: Rarity based Outlier Detection in a Sparse Coding Framework
Abstract— RODS: Rarity based Outlier Detection in a Sparse Coding Framework. Outlier detection has been an active area of research for few decades. We propose a new definition of outlier useful for high-dimensional data. Given a dictionary of atoms learned using the sparse coding objective, the outlierness of a data point depends jointly on two factors: the frequency of each atom in reconstructing all data points < Final Year Projects 2016 > and the strength by which it is used in reconstructing the current point. A Rarity based Outlier Detection algorithm in a Sparse coding framework (RODS) is developed that consists of two components, NLAR learning and outlier scoring. The algorithm is unsupervised; both the offline and online variants are presented. It is governed by very few parameters and operates in linear time.
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