We propose a detection method of corner outlier artifacts and a simple and effective filter in order to remove the artifacts in highly compressed video. We detect the corner outlier artifacts based on the direction of edges going through a block corner and the properties of blocks around the edges. Based on the detection results, we remove the stair-shaped discontinuities, i.e., corner outlier ...

Frequent pattern outlier factor is used to detect outliers with complete frequent itemsets. But it is difficult in real-world time-series data streams application because of its low efficiency. In this paper, we propose a novel maximal frequent pattern outlier factor (MFPOF) and an outlier detection algorithm (OODFP) for online high-dimensional time-series outlier detection. Firstly, the time-s...

This paper deals with outlier modeling within a very special framework: a segment-based speech recognizer. The recognizer is built on a neural net that, besides classifying speech segments, has to identify outliers as well. One possibility is to artificially generate outlier samples, but this is tedious, error-prone and significantly increases the training time. This study examines the alternat...

Outlier Mining has always attract much attention among the data mining community. This paper discusses on the discovery of meaningful outlier detection based on Non_Reduct computation by incorporating the Negative Association Rules. Non_Reduct computation is proposed to detect outliers from rare classes. These outliers may have meaningful knowledge having incorporating the concept of Negatives ...

Outlier or anomaly detection in large data sets is a fundamental task in data science, with broad applications. However, in real data sets with high-dimensional space, most outliers are hidden in certain dimensional combinations and are relative to a user's search space and interest. It is often more effective to give power to users and allow them to specify outlier queries flexibly, and the sy...

We searched a gene expression dataset comprised of 634 schizophrenia (SZ) cases and 713 controls for expression outliers (i.e., extreme tails of the distribution of transcript expression values) with SZ cases overrepresented compared with controls. These outlier genes were enriched for brain expression and for genes known to be associated with neurodevelopmental disorders. SZ cases showed highe...

We propose an inlier-based outlier detection method capable of both identifying the outliers and explaining why they are outliers, by identifying the outlier-specific features. Specifically, we employ an inlier-based outlier detection criterion, which uses the ratio of inlier and test probability densities as a measure of plausibility of being an outlier. For estimating the density ratio functi...

Outlier detection has recently become an important problem in many data mining applications. In this paper, a novel unsupervised algorithm for outlier detection is proposed. First we apply a provably globally optimal Expectation Maximization (EM) algorithm to fit a Gaussian Mixture Model (GMM) to a given data set. In our approach, a Gaussian is centered at each data point, and hence, the estima...

Outlier detection methods have used approximate neighborhoods in filter-refinement approaches. Outlier detection ensembles have used artificially obfuscated neighborhoods to achieve diverse ensemble members. Here we argue that outlier detection models could be based on approximate neighborhoods in the first place, thus gaining in both efficiency and effectiveness. It depends, however, on the ty...

Outlier genes with marked overexpression in subsets of cancers like ERBB2 have potential for the identification of gene classifiers and therapeutic targets for the appropriate subpopulation. In this study, using the cancer outlier profile analysis strategy, we identified WNT1-inducible-signaling pathway protein 3 (WISP3) as an outlier gene that is highly expressed in a subset of colorectal canc...