Multi-view learning aims to combine multiple features to achieve more comprehensive
descriptions of data. Most previous works assume that multiple views are strictly aligned …

Though the sheer volume of data that is collected is immense, it is the rare categories that
are often the most important in many high-impact domains, ranging from financial fraud …

The explosion of modeling complex systems using attributed networks boosts the research
on anomaly detection in such networks, which can be applied in various high-impact …

Identifying different types of data outliers with abnormal behaviors in multi-view data setting
is challenging due to the complicated data distributions across different views. Conventional …

In any dataset, events which deviate from the majority of regular patterns are called as rare
events. These events can be any unusual activity, fraud, intrusion or suspicious abnormal …

Recently, multi-view outlier detection attracts increasingly more attention. Although existing
multi-view outlier detection methods have demonstrated promising performance, they still …

Multi-view or even multi-modal data is appealing yet challenging for real-world applications.
Detecting anomalies in multi-view data is a prominent recent research topic. However, most …

Contemporary datasets are often comprised of multiple views of data, which provide
complete and complementary information in different views, and multi-view clustering is one …

Detecting outliers or anomalies is a fundamental problem in various machine learning and
data mining applications. Conventional outlier detection algorithms are mainly designed for …

Outlier detection is a fundamental problem in data mining. Unlike most existing methods that
are designed for single-view data, we propose a multi-view outlier detection approach in this …