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      Data imputation through the identification of local anomalies

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      Author
      Ozkan, H.
      Pelvan, O. S.
      Kozat, S. S.
      Date
      2015
      Source Title
      IEEE Transactions on Neural Networks and Learning Systems
      Print ISSN
      2162-237X
      Publisher
      Institute of Electrical and Electronics Engineers Inc.
      Volume
      26
      Issue
      10
      Pages
      2381 - 2395
      Language
      English
      Type
      Article
      Item Usage Stats
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      94
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      Abstract
      We introduce a comprehensive and statistical framework in a model free setting for a complete treatment of localized data corruptions due to severe noise sources, e.g., an occluder in the case of a visual recording. Within this framework, we propose: 1) a novel algorithm to efficiently separate, i.e., detect and localize, possible corruptions from a given suspicious data instance and 2) a maximum a posteriori estimator to impute the corrupted data. As a generalization to Euclidean distance, we also propose a novel distance measure, which is based on the ranked deviations among the data attributes and empirically shown to be superior in separating the corruptions. Our algorithm first splits the suspicious instance into parts through a binary partitioning tree in the space of data attributes and iteratively tests those parts to detect local anomalies using the nominal statistics extracted from an uncorrupted (clean) reference data set. Once each part is labeled as anomalous versus normal, the corresponding binary patterns over this tree that characterize corruptions are identified and the affected attributes are imputed. Under a certain conditional independency structure assumed for the binary patterns, we analytically show that the false alarm rate of the introduced algorithm in detecting the corruptions is independent of the data and can be directly set without any parameter tuning. The proposed framework is tested over several well-known machine learning data sets with synthetically generated corruptions and experimentally shown to produce remarkable improvements in terms of classification purposes with strong corruption separation capabilities. Our experiments also indicate that the proposed algorithms outperform the typical approaches and are robust to varying training phase conditions. © 2015 IEEE.
      Keywords
      Anomaly detection
      localized corruption
      Algorithms
      Artificial intelligence
      Binary trees
      Classification (of information)
      Crime
      Forestry
      Iterative methods
      Learning systems
      Statistical tests
      Anomaly detection
      Euclidean distance
      localized corruption
      Maximum a posteriori
      Maximum a Posteriori Estimator
      occlusion
      Parameter-tuning
      Statistical framework
      Trees (mathematics)
      Permalink
      http://hdl.handle.net/11693/20881
      Published Version (Please cite this version)
      http://dx.doi.org/10.1109/TNNLS.2014.2382606
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      • Department of Electrical and Electronics Engineering 3524
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