How to tackle high dimensionality of data effectively and efficiently is still a challenging issue in machine learning. Identifying anomalous objects from given data has a broad range of real-world applications. Although many classical outlier detection or ranking algorithms have been witnessed during the past years, the high-dimensional problem, as well as the size of neighborhood, in outlier detection have not yet attracted sufficient attention. The former may trigger the distance concentration problem that the distances of observations in high-dimensional space tend to be indiscernible, whereas the latter requires appropriate values for parameters, making models high complex and more sensitive. To partially circumvent these problems, especially the high dimensionality, we introduce a concept called local projection score (LPS) to represent deviation degree of an observation to its neighbors. The LPS is obtained from the neighborhood information by the technique of low-rank approximation. The observation with high LPS is a promising candidate of outlier in high probability. Based on this notion, we propose an efficient and effective outlier detection algorithm, which is also robust to the parameter k of k nearest neighbors. Extensive evaluation experiments conducted on twelve public real-world data sets with five popular outlier detection algorithms show that the performance of the proposed method is competitive and promising.

Outlier detection is a hot topic in machine learning. With the newly emerging technologies and diverse applications, the interest of outlier detection is increasing greatly. Recently, a significant number of outlier detection methods have been witnessed and successfully applied in a wide range of fields, including medical health, credit card fraud and intrusion detection. They can be used for conventional data analysis. However, it is not a trivial work to identify rare behaviors or patterns out from complicated data. In this paper, we provide a brief overview of the outlier detection methods for high-dimensional data, and offer comprehensive understanding of the-state-of-the-art techniques of outlier detection for practitioners. Specifically, we firstly summarize the recent advances on outlier detection for high-dimensional data, and then make an extensive experimental comparison to the popular detection methods on public datasets. Finally, several challenging issues and future research directions are discussed.

Graphs are a tremendously suitable data representation that models the relationships of entities in many application domains, such as recommendation systems, machine learning, computational biology, social network analysis, and other application domains. Graphs with many vertices and edges have become quite prevalent in recent years. Therefore, graph computing systems with integrated various graph partitioning techniques have been envisioned as a promising paradigm to handle large-scale graph analytics in these application domains. However, scalable processing of large-scale graphs is challenging due to their high volume and inherent irregular structure of the real-world graphs. Hence, industry and academia have recently proposed graph partitioning and computing systems to efficiently process and analyze large-scale graphs. The graph partitioning and computing systems have been designed to improve scalability issues and reduce processing time complexity. This paper presents an overview, classification, and investigation of the most popular graph partitioning and computing systems. The various methods and approaches of graph partitioning and diverse categories of graph computing systems are presented. Finally, we discuss future challenges and research directions in graph partitioning and computing systems.