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» Outlier Detection by Rareness Assumption
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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...
ICRA
2007
IEEE
199views Robotics» more  ICRA 2007»
14 years 1 months ago
Automatic Outlier Detection: A Bayesian Approach
— In order to achieve reliable autonomous control in advanced robotic systems like entertainment robots, assistive robots, humanoid robots and autonomous vehicles, sensory data n...
Jo-Anne Ting, Aaron D'Souza, Stefan Schaal
KDD
2007
ACM
141views Data Mining» more  KDD 2007»
14 years 8 months ago
Detecting anomalous records in categorical datasets
We consider the problem of detecting anomalies in high arity categorical datasets. In most applications, anomalies are defined as data points that are 'abnormal'. Quite ...
Kaustav Das, Jeff G. Schneider
CVPR
2011
IEEE
12 years 11 months ago
Max-margin Clustering: Detecting Margins from Projections of Points on Lines
Given a unlabelled set of points X ∈ RN belonging to k groups, we propose a method to identify cluster assignments that provides maximum separating margin among the clusters. We...
Raghuraman Gopalan, Jagan Sankaranarayanan
ECCV
2010
Springer
13 years 10 months ago
Fast dynamic texture detection
Dynamic textures can be considered to be spatio-temporally varying visual patterns in image sequences with certain temporal regularity. We propose a novel and efficient approach t...