A0932
Title: Abnormal component analysis
Authors: Pavlo Mozharovskyi - LTCI, Telecom Paris, Institut Polytechnique de Paris (France) [presenting]
Romain Valla - Telecom Paris, Institut Polytechnique de Paris (France)
Florence d Alche-Buc - Telecom Paris (France)
Abstract: At the crossroads of machine learning and data analysis, anomaly detection aims at identifying observations that exhibit abnormal behavior. Be it measurement errors, disease development, severe weather, production quality default(s) (items) or failed equipment, financial frauds, or crisis events, their on-time identification and isolation constitute an important task in almost any area of industry and science. While a substantial body of literature is devoted to the detection of anomalies, little attention is paid to their explanation. This is the case mostly due to the intrinsically non-supervised nature of the task and the non-robustness of the exploratory methods like principal component analysis (PCA). A new statistical tool is introduced, dedicated to exploratory analysis of abnormal observations using data depth as a score. Abnormal component analysis (shortly ACA) is a method that searches a low-dimensional data representation that best visualizes and explains anomalies. This low-dimensional representation not only allows to distinguish groups of anomalies better than the methods of the state of the art, but as well provides a linear in variables and thus easily interpretable explanation for anomalies.
