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Inconsistency and OutliersActive Learning by Outlier DetectionInconsistency Robustness Symposium 2011<br />Neil Rubens<br ...
Outline<br />Inconsistency Robustness is a multi-disciplinary issue.  We discuss some of the aspect of Inconsistency Robus...
Inconsistency-Outlier<br />Inconsistency/outlier: data that does not agree with the model.<br />
Outlier Types<br />Spatial Outlier<br />unlabeled data<br />Our Focus<br />Model Outlier<br />labeled data<br />
Causes of Outliers<br />Faulty data<br />Entry error, malfunction, etc.<br />Chance/Deviation<br />Incorrect Model<br />Ou...
Typical Treatment of Outliers<br />Assume that the learned model is correct and discard points that don’t agree with the m...
Atypical Treatment of Outliers<br />Assume that data is right, and that the model is wrong<br />Our Focus<br />
Rubens et al, AJS 2011<br />
If there is no inconsistency between the training and testing data then<br /> the most complex model would tend be selecte...
Change Detection / Model Correction <br />Is inconsistency caused by noise (or minor factors) or by changes in the underly...
Conclusion<br />Inconsistency could be useful for:<br />Hypothesis Learning<br />Model Selection<br />Model Correction<br ...
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Inconsistent Outliers

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Outliers and Inconsistency at Inconsistency Robustness Symposium 2011 at Stanford University.

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Inconsistent Outliers

  1. 1. Inconsistency and OutliersActive Learning by Outlier DetectionInconsistency Robustness Symposium 2011<br />Neil Rubens<br />Assistant Professor<br />University of Electro-Communications<br />Tokyo, Japan<br />
  2. 2. Outline<br />Inconsistency Robustness is a multi-disciplinary issue. We discuss some of the aspect of Inconsistency Robustness from the perspective of Machine Learning:<br />What is Inconsistency<br />Can Inconsistency be Useful<br />Measuring Inconsistency<br />
  3. 3. Inconsistency-Outlier<br />Inconsistency/outlier: data that does not agree with the model.<br />
  4. 4. Outlier Types<br />Spatial Outlier<br />unlabeled data<br />Our Focus<br />Model Outlier<br />labeled data<br />
  5. 5. Causes of Outliers<br />Faulty data<br />Entry error, malfunction, etc.<br />Chance/Deviation<br />Incorrect Model<br />Our Focus<br />http://www.dkimages.com/discover/previews/852/20223083.JPG<br />
  6. 6. Typical Treatment of Outliers<br />Assume that the learned model is correct and discard points that don’t agree with the model<br />
  7. 7. Atypical Treatment of Outliers<br />Assume that data is right, and that the model is wrong<br />Our Focus<br />
  8. 8.
  9. 9.
  10. 10.
  11. 11. Rubens et al, AJS 2011<br />
  12. 12.
  13. 13. If there is no inconsistency between the training and testing data then<br /> the most complex model would tend be selected.<br />
  14. 14. Change Detection / Model Correction <br />Is inconsistency caused by noise (or minor factors) or by changes in the underlying model<br />http://www.skyboximaging.com/solutions/application/change-detection<br />Applications: medical diagnostics, intrusion detection, network analysis, finance<br />http://www.satimagingcorp.com/galleryimages/high-resolution-landsat-satellite-imagery-oman.jpg<br />http://www.lucieer.net/research/heard.html<br />http://www.ittvis.com/portals/0/images/ChangeDetection_3window.jpg<br />
  15. 15. Conclusion<br />Inconsistency could be useful for:<br />Hypothesis Learning<br />Model Selection<br />Model Correction<br />Neil Rubens<br />Assistant ProfessorActive Intelligence Group<br />Laboratory for Knowledge Computing<br />University of Electro-Communications<br />Tokyo, Japan<br />http://ActiveIntelligence.org<br />

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