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ICASSP
2011
IEEE
13 years 1 months ago
Robust nonparametric regression by controlling sparsity
Nonparametric methods are widely applicable to statistical learning problems, since they rely on a few modeling assumptions. In this context, the fresh look advocated here permeat...
Gonzalo Mateos, Georgios B. Giannakis
CORR
2011
Springer
190views Education» more  CORR 2011»
13 years 1 months ago
Doubly Robust Smoothing of Dynamical Processes via Outlier Sparsity Constraints
Abstract—Coping with outliers contaminating dynamical processes is of major importance in various applications because mismatches from nominal models are not uncommon in practice...
Shahrokh Farahmand, Georgios B. Giannakis, Daniele...
IDEAL
2010
Springer
13 years 7 months ago
Robust 1-Norm Soft Margin Smooth Support Vector Machine
Based on studies and experiments on the loss term of SVMs, we argue that 1-norm measurement is better than 2-norm measurement for outlier resistance. Thus, we modify the previous 2...
Li-Jen Chien, Yuh-Jye Lee, Zhi-Peng Kao, Chih-Chen...
ICDM
2010
IEEE
216views Data Mining» more  ICDM 2010»
13 years 8 months ago
Data Editing Techniques to Allow the Application of Distance-Based Outlier Detection to Streams
The problem of finding outliers in data has broad applications in areas as diverse as data cleaning, fraud detection, network monitoring, invasive species monitoring, etc. While th...
Vit Niennattrakul, Eamonn J. Keogh, Chotirat Ann R...
PVLDB
2010
117views more  PVLDB 2010»
13 years 8 months ago
Distance-Based Outlier Detection: Consolidation and Renewed Bearing
Detecting outliers in data is an important problem with interesting applications in a myriad of domains ranging from data cleaning to financial fraud detection and from network i...
Gustavo Henrique Orair, Carlos Teixeira, Ye Wang, ...
IJDAR
2002
116views more  IJDAR 2002»
13 years 9 months ago
Performance evaluation of pattern classifiers for handwritten character recognition
Abstract. This paper describes a performance evaluation study in which some efficient classifiers are tested in handwritten digit recognition. The evaluated classifiers include a s...
Cheng-Lin Liu, Hiroshi Sako, Hiromichi Fujisawa
KAIS
2007
120views more  KAIS 2007»
13 years 10 months ago
Capabilities of outlier detection schemes in large datasets, framework and methodologies
Abstract. Outlier detection is concerned with discovering exceptional behaviors of objects. Its theoretical principle and practical implementation lay a foundation for some importa...
Jian Tang, Zhixiang Chen, Ada Wai-Chee Fu, David W...
DATAMINE
2006
164views more  DATAMINE 2006»
13 years 10 months ago
Fast Distributed Outlier Detection in Mixed-Attribute Data Sets
Efficiently detecting outliers or anomalies is an important problem in many areas of science, medicine and information technology. Applications range from data cleaning to clinica...
Matthew Eric Otey, Amol Ghoting, Srinivasan Partha...
CSDA
2008
147views more  CSDA 2008»
13 years 10 months ago
An adjusted boxplot for skewed distributions
The boxplot is a very popular graphical tool to visualize the distribution of continuous unimodal data. It shows information about the location, spread, skewness as well as the ta...
M. Hubert, E. Vandervieren
CSDA
2008
158views more  CSDA 2008»
13 years 10 months ago
Outlier identification in high dimensions
A computationally fast procedure for identifying outliers is presented, that is particularly effective in high dimensions. This algorithm utilizes simple properties of principal c...
Peter Filzmoser, Ricardo A. Maronna, Mark Werner