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712
Handling of data containing outliers
, 2010
"... 1 PCA robust to outliers Away from often showing missing values, Microarray or Metabolite data are often corrupted with extreme values (outliers). Standard SVD is highly susceptible to outliers. In the extreme case, an individual data point, if sufficiently outlying, can draw even the leading princi ..."
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1 PCA robust to outliers Away from often showing missing values, Microarray or Metabolite data are often corrupted with extreme values (outliers). Standard SVD is highly susceptible to outliers. In the extreme case, an individual data point, if sufficiently outlying, can draw even the leading
Abstract Suboptimal LULU-estimators in Measurements Containing Outliers
, 2013
"... By submitting this thesis electronically, I declare that the entirety of the work contained therein is my own, original work, that I am the sole author thereof (save to the extent explicitly otherwise stated), that reproduction and publication thereof by Stellenbosch University will not infringe any ..."
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By submitting this thesis electronically, I declare that the entirety of the work contained therein is my own, original work, that I am the sole author thereof (save to the extent explicitly otherwise stated), that reproduction and publication thereof by Stellenbosch University will not infringe
Regression Analysis for Data Containing Outliers and High Leverage Points
"... The strong impact of outliers and leverage points on the ordinary least square (OLS) regression estimator is studied for a long time. Situations in which a relatively small percentage of the data has a significant impact on the model may not be acceptable to the user of the model. A vast literature ..."
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The strong impact of outliers and leverage points on the ordinary least square (OLS) regression estimator is studied for a long time. Situations in which a relatively small percentage of the data has a significant impact on the model may not be acceptable to the user of the model. A vast literature
Efficient Algorithms for Mining Outliers from Large Data Sets
, 2000
"... In this paper, we propose a novel formulation for distance-based outliers that is based on the distance of a point from its k th nearest neighbor. We rank each point on the basis of its distance to its k th nearest neighbor and declare the top n points in this ranking to be outliers. In addition ..."
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Cited by 322 (0 self)
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, and then prunes entire partitions as soon as it is determined that they cannot contain outliers. This results in substantial savings in computation. We present the results of an extensive experimental study on real-life and synthetic data sets. The results from a real-life NBA database highlight and reveal
Mining Outliers in Spatial Networks
"... Abstract. Outlier analysis is an important task in data mining and has attracted much attention in both research and applications. Previous work on outlier detection involves different types of databases such as spatial databases, time series databases, biomedical databases, etc. However, few of the ..."
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Cited by 1 (0 self)
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, then quickly identifies the outliers in the remaining edges after pruning those unnecessary edges which cannot contain outliers. We also present algorithms that can be applied when the spatial network is updating points or the input parameters of outlier measures are changed. The experimental results verify
FP-Outlier: frequent pattern based outlier detection
, 2002
"... An outlier in a dataset is an observation or a point that is considerably dissimilar to or inconsistent with the remainder of the data. Detection of such outliers is important for many applications and has recently attracted much attention in the data mining research community. In this paper, we pr ..."
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Cited by 6 (0 self)
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present a new method to detect outliers by discovering frequent patterns (or frequent itemsets) from the data set. The outliers are defined as the data transactions that contain less frequent patterns in their itemsets. We define a measure called FPOF (Frequent Pattern Outlier Factor) to detect
Learning an Outlier-Robust Kalman Filter
"... Abstract. We introduce a modified Kalman filter that performs robust, real-time outlier detection, without the need for manual parameter tuning by the user. Systems that rely on high quality sensory data (for instance, robotic systems) can be sensitive to data containing outliers. The standard Kalma ..."
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Cited by 6 (0 self)
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Abstract. We introduce a modified Kalman filter that performs robust, real-time outlier detection, without the need for manual parameter tuning by the user. Systems that rely on high quality sensory data (for instance, robotic systems) can be sensitive to data containing outliers. The standard
D-optimality for minimum volume ellipsoid with outliers
- In Proceedings of the Seventh International Conference on Signal/Image Processing and Pattern Recognition, (UkrOBRAZ’2004
, 2004
"... A family of one-class classification methods is extended by the determinant maximization novelty detection (DMND) model based on the D-optimum experimental design approach for the ellipsoid estimation. Similar to the one-class classification methods based on the support vector machine or the so-call ..."
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Cited by 5 (3 self)
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-called support vector data description (SVDD) approach, DMND is a method that fits a geometrical object around the training data. However, in contrast to SVDD, DMND finds the hyperellipsoid of the smallest volume covering the target objects that can contain outliers by maximizing the determinant
Multivariate Outlier Detection Using Independent Component Analysis
- Science Journal of Applied Mathematics and Statistics, Science Publishing Group, USA
"... Abstract: The recent developments by considering a rather unexpected application of the theory of Independent component analysis (ICA) found in outlier detection, data clustering and multivariate data visualization etc. Accurate identification of outliers plays an important role in statistical analy ..."
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Cited by 2 (2 self)
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analysis. If classical statistical models are blindly applied to data containing outliers, the results can be misleading at best. In addition, outliers themselves are often the special points of interest in many practical situations and their identification is the main purpose of the investigation
Variance estimation for complex surveys in the presence of outliers
- In Proceedings of the Section on Survey Research Methods
, 2006
"... Quantitative variables in surveys often have a markedly skew distribution and, in addition, contain outliers. Robust estima-tors, which may be used in this situation, generally are bi-ased. In addition linearized variance estimators tend to un-derestimate the true variance considerably. Alternatives ..."
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Cited by 1 (0 self)
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Quantitative variables in surveys often have a markedly skew distribution and, in addition, contain outliers. Robust estima-tors, which may be used in this situation, generally are bi-ased. In addition linearized variance estimators tend to un-derestimate the true variance considerably
Results 1 - 10
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712
