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TSMC
2011
228views more  TSMC 2011»
13 years 2 months ago
Privacy-Preserving Outlier Detection Through Random Nonlinear Data Distortion
— Consider a scenario in which the data owner has some private/sensitive data and wants a data miner to access it for studying important patterns without revealing the sensitive ...
Kanishka Bhaduri, Mark D. Stefanski, Ashok N. Sriv...
TSMC
2002
107views more  TSMC 2002»
13 years 7 months ago
Guaranteed robust nonlinear estimation with application to robot localization
When reliable prior bounds on the acceptable errors between the data and corresponding model outputs are available, bounded-error estimation techniques make it possible to characte...
Luc Jaulin, Michel Kieffer, Eric Walter, Dominique...
ICDM
2008
IEEE
176views Data Mining» more  ICDM 2008»
14 years 1 months ago
Inlier-Based Outlier Detection via Direct Density Ratio Estimation
We propose a new statistical approach to the problem of inlier-based outlier detection, i.e., finding outliers in the test set based on the training set consisting only of inlier...
Shohei Hido, Yuta Tsuboi, Hisashi Kashima, Masashi...
KDD
2009
ACM
189views Data Mining» more  KDD 2009»
14 years 2 months ago
CoCo: coding cost for parameter-free outlier detection
How can we automatically spot all outstanding observations in a data set? This question arises in a large variety of applications, e.g. in economy, biology and medicine. Existing ...
Christian Böhm, Katrin Haegler, Nikola S. M&u...
IROS
2007
IEEE
171views Robotics» more  IROS 2007»
14 years 1 months ago
A Kalman filter for robust outlier detection
— In this paper, we introduce a modified Kalman filter that can perform robust, real-time outlier detection in the observations, without the need for parameter tuning. Robotic ...
Jo-Anne Ting, Evangelos Theodorou, Stefan Schaal