While real-world data are often grossly corrupted, most techniques of blind source separation (BSS) give erroneous results in the presence of outliers. We propose a robust algorithm that jointly estimates the sparse sources and outliers without requiring any prior knowledge on the outliers. More precisely, it uses an alternative weighted scheme to weaken the influence of the estimated outliers....

Outlier detection plays an essential role in many data-driven applications to identify isolated instances that are di‚erent from the majority. While many statistical learning and data mining techniques have been used for developing more e‚ective outlier detection algorithms, the interpretation of detected outliers does not receive much aŠention. Interpretation is becoming increasingly important...

The mining of outliers (or anomaly detection) in large databases continues to remain an active area of research with many potential applications. Over the last several years many novel methods have been proposed to efficiently and accurately mine for outliers. In this paper we propose a unique approach to mine for sequential outliers using Probabilistic Suffix Trees (PST). The key insight that ...

Social outliers draw a lot of attention from those inside and outside their group and yet little is known about their impact on perceptions of their group as a whole. The present studies examine how outliers influence observers' summary perceptions of a group's behavior and inferences about the group's descriptive and prescriptive norms. Across 4 studies (N = 1,718) we examine how observers per...

This paper proposes an algorithm for rejecting false matches (known as outliers) in image pairs acquired with automobile-mounted cameras. Many intelligent vehicle applications require point correspondences for motion estimation and 3D reconstruction which can be affected by outliers. We use the property of automobile motion to reject outliers. Automobile motion mostly represented by two transla...

An outlier is an observation that deviates so much from other observations as to arouse suspicion that it was generated by a different mechanism (Hawkins, 1980). Outlier detection has many applications, such as data cleaning, fraud detection and network intrusion. The existence of outliers can indicate individuals or groups that have behavior very different from the most of the individuals of t...

Outlier Detection is a major issue in data mining. Outliers are the containments that divert from the other objects. Outlier detection is used to make the data knowledgeable, and easy to understand. There are many type of databases used now days, and many of them contains anomaly objects, detection or removal of these objects is known as outlier detection. In the proposed work outliers are dete...

This paper deals with nding outliers (exceptions) in large datasets. The identiication of outliers can often lead to the discovery of truly unexpected knowledge in areas such as electronic commerce, credit card fraud, and even the analysis of performance statistics of professional athletes. One contribution of this paper is to show how our proposed, intuitive notion of outliers can unify or gen...

In tracer kinetic studies for extracting biological information, measurement noise and error in the kinetics could affect the reliability of the estimated biological parameters. While the effect of statistical noise has been studied extensively before, the effect of outliers has not been addressed as much. In this study, we described an algorithm for detecting and removing outliers. Computer si...

An outlier is an observation that deviates so much from other observations as to arouse suspicion that it was generated by a different mechanism (Hawkins, 1980). Outlier detection has many applications, such as data cleaning, Fraud detection and network intrusion. The existence of outliers can indicate individuals or groups that have behavior very different to the most of the individuals of the...