We propose an original method for detecting and localizing anomalous motion patterns in videos from a camera view-based motion representation perspective. Anomalous motion should be taken in a broad sense, i.e., unexpected, abnormal, singular, irregular, or unusual motion. Identifying distinctive dynamic information at any time point and at any image location in a sequence of images is a key re...

Jinshan Pan, Zhouchen Lin , Zhixun Su, Ming-Hsuan Yang School of Mathematical Sciences, Dalian University of Technology Electrical Engineering and Computer Science, University of California at Merced Key Laboratory of Machine Perception (MOE), School of EECS, Peking University Cooperative Medianet Innovation Center, Shanghai Jiaotong University National Engineering Research Center of Digital Li...

Operational optimization is essential in modern industry and unsuitable operations will deteriorate the performance of industrial processes. Since measuring error multiple working conditions are inevitable practice, it necessary to reduce their negative impacts on operational under case-based reasoning (CBR) framework. In this paper, a local density-based abnormal case removal method proposed r...

Numerous applications in dynamic social networks, ranging from telecommunications to financial transactions, create evolving datasets. Detecting outliers in such dynamic networks is inherently challenging, because the arbitrary linkage structure with massive information is changing over time. Little research has been done on detecting outliers for dynamic social networks, even then, they repres...

In this paper, we propose an outlier detection approach based on local kernel regression for instance selection. It evaluates the reconstruction error of instances by their neighbors to identify the outliers. Experiments are performed on the synthetic and real data sets to show the efficacy of the proposed approach in comparison with the existing counterparts.

Selecting features is an important step of any machine learning task, though most of the focus has been to choose features relevant for classification and regression. In this work, we present a novel non-parametric evaluation criterion for filter-based feature selection which enhances outlier detection. Our proposed method seeks the subset of features that represents the inherent characteristic...

In the real world, various systems can be modeled using entity-relationship graphs. Given such a graph, one may be interested in identifying suspicious or anomalous subgraphs. Specifically, a user may want to identify suspicious subgraphs matching a query template. A subgraph can be defined as anomalous based on the connectivity structure within itself as well as with its neighborhood. For exam...

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...