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Sample of Target Class Possible Outlier
"... Our contribution in oneclass classification (OCC) Probabilistic framework based on information theory ⇒ OCC from a completely different perspective Impact of the label on the current model assumed label reclassification result current model Is this an example of a novel category? novelty score tes ..."
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Our contribution in oneclass classification (OCC) Probabilistic framework based on information theory ⇒ OCC from a completely different perspective Impact of the label on the current model assumed label reclassification result current model Is this an example of a novel category? novelty score tes t s am ple tra ini ng se t Measure how strongly a new test sample would influence the current model if it was used for training Estimate the model change by comparing reclassification results using mutual information ︸ ︷ ︷ ︸
Some new estimation methods for weighted regression when there are possible outliers
 Technometrics
, 1986
"... The problem of estimating the variance parameter robustly in a heteroscedatic linear model is considered. The situation where the variance is a function of the explanatory variables is treated. To estimate the variance robustly in this case, it is necessary to guard against the influence of outliers ..."
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Cited by 6 (0 self)
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of outliers in the design as well as outliers in the response. By analogy with the homoscedastic regression case, two estimators are proposed which do this. Their performance is evaluated on a number of data sets. We had considerable success with estimators that bound the "selfinfluence", that is
SOME NEW ESTIMATION METHODS FOR WEIGHTED REGRESSION WHEN THERE ARE POSSIBLE OUTLIERS
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SAS Macros for Analysis of Unreplicated 2k and 2k−p Designs with a Possible Outlier
"... Many techniques have been proposed for judging the significance of effects in unreplicated 2k and 2k−p designs. However, relatively few methods have been proposed for analyzing unreplicated designs with possible outliers. Outliers can be a major impediment to valid interpretation of data from unrep ..."
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Many techniques have been proposed for judging the significance of effects in unreplicated 2k and 2k−p designs. However, relatively few methods have been proposed for analyzing unreplicated designs with possible outliers. Outliers can be a major impediment to valid interpretation of data from
Outlier Finding: Focusing User Attention on Possible Errors
, 2001
"... When users handle large amounts of data, errors are hard to notice. Outlier finding is a new way to reduce errors by directing the user's attention to inconsistent data which may indicate errors. We have implemented an outlier finder for text, which can detect both unusual matches and unusual m ..."
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Cited by 45 (5 self)
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When users handle large amounts of data, errors are hard to notice. Outlier finding is a new way to reduce errors by directing the user's attention to inconsistent data which may indicate errors. We have implemented an outlier finder for text, which can detect both unusual matches and unusual
Matching in Catadioptric Images with Appropriate Windows, and Outliers Removal
, 2001
"... Active matching windows for matching in panoramic images taken by a catadioptric camera are proposed. The shape and the size of the windows vary depending on the position of an interest point. The windows size is then normalized and a standard correlation is used for measuring similarities of th ..."
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Cited by 14 (0 self)
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of the points. A semiiterative method based on sorting correspondences according to their similarity is suggested to remove possible outliers. It is experimentally shown that using this method the matching is successful for small and also big displacement of corresponding points.
NEW PROPOSALS IN MULTIVARIATE OUTLIERS IDENTIFICATION
"... ABSTRACT: Occurrences of outliers in multivariate time series are unpredictable events which may severely distort the analysis of the series. It may be noticed that a convenient way for representing multiple outliers consists in superimposing a deterministic disturbance to a Gaussian multivariate ti ..."
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time series. Then outliers may be modelled as non – Gaussian time series components. The independent component analysis is a recently developed tool that is likely to be able to extract possible outlier patterns. In practice the independent component analysis may be used to analyze multivariate
OUTLIERS AND TREND DETECTION TESTS IN RAINFALL EXTREMES.
"... In the present work daily rainfall time series is analyzed using Extreme Value methodologies. Four different tests are used for detection of a possible outlier: the estimation of xult, the conditional probability of the event estimated through a Bayesian formula, a Gumbel plot and a qq plot for an ..."
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In the present work daily rainfall time series is analyzed using Extreme Value methodologies. Four different tests are used for detection of a possible outlier: the estimation of xult, the conditional probability of the event estimated through a Bayesian formula, a Gumbel plot and a qq plot
Detection of Outlier Patches in Autoregressive Time Series
, 1998
"... This paper proposes a procedure to detect patches of outliers in an autoregressive process. The procedure is an improvement over the existing detection methods via Gibbs sampling. We show that the standard outlier detection via Gibbs sampling may become extremely inefficient in the presence of sever ..."
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Cited by 11 (1 self)
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of severe masking and swamping effects. The new procedure identifies the beginning and end of possible outlier patches using the existing Gibbs sampling, then carries out an adaptive procedure with block interpolation to handle patches of outliers. Empirical and simulated examples show that the proposed
Robust Matrix Decomposition with Outliers
, 2010
"... Suppose a given observation matrix can be decomposed as the sum of a lowrank matrix and a sparse matrix (outliers), and the goal is to recover these individual components from the observed sum. Such additive decompositions have applications in a variety of numerical problems including system identi ..."
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Cited by 16 (1 self)
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identification, latent variable graphical modeling, and principal components analysis. We study conditions under which recovering such a decomposition is possible via a combination of ℓ1 norm and trace norm minimization. We are specifically interested in the question of how many outliers are allowed so
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