Outliers as well as outlier patches, which widely emerge in dynamic process sampling data series, have strong bad influence on signal processing. In this paper, a series of recursive outlier-tolerant fitting algorithms are built to fit reliably the trajectories of a non-stationary sampling process when there are some outliers arising from output components of the process. Based on the recursive...

In regression analysis, the presence of outliers in the data set can strongly distort the classical least squares estimator and lead to unreliable results. To deal with this, several robust-to-outliers methods have been proposed in the statistical literature. In Stata, some of these methods are available through the commands rreg and qreg. Unfortunately, these methods only resist to some specif...

This application note will benefit chemical and material manufacturers concerned about micron and submicron par ticle contamination. Outliers are a common problem in many industrial applications. Outliers can be defined as particles that are significantly larger or smaller than the main particle size distribution, but which occur in a much lower frequency than the main par ticle size. As a simp...

An outlier is an observation that deviates so much from other observations that it seems to have been generated by a different mechanism. Outlier detection has many applications, such as data cleaning, fraud detection and network intrusion. The existence of outliers can indicate individuals or groups that exhibit a behavior that is very different from most of the individuals of the data set. Fr...

Outliers are dissimilar or inconsistent data objects with respect to the remaining data objects in the data set or which are far away from their cluster centroids. Detecting outliers in data is a very important concept in Knowledge Data Discovery process for finding hidden knowledge. The task of detecting the outliers has been studied in a large number of research areas like Financial Data Anal...

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 present a new method to detect outliers by discovering frequent patterns (or frequent itemsets) from...

Genome scans are widely used to identify 'outliers' in genomic data: loci with different patterns compared with the rest of the genome due to the action of selection or other nonadaptive forces of evolution. These genomic data sets are often high dimensional, with complex correlation structures among variables, making it a challenge to identify outliers in a robust way. The Mahalanobis distance...

Principal Component Analysis (PCA) has been widely used for the representation of shape, appearance, and motion. One drawback of typical PCA methods is that they are least squares estimation techniques and hence fail to account for “outliers” which are common in realistic training sets. In computer vision applications, outliers typically occur within a sample (image) due to pixels that are corr...