  One-class classification (OCC), which models one single positive class and
distinguishes it from the negative class, has been a long-standing topic with
pivotal application to realms like anomaly detection. As modern society often
deals with massive high-dimensional complex data spawned by multiple sources,
it is natural to consider OCC from the perspective of multi-view deep learning.
However, it has not been discussed by the literature and remains an unexplored
topic. Motivated by this blank, this paper makes four-fold contributions:
First, to our best knowledge, this is the first work that formally identifies
and formulates the multi-view deep OCC problem. Second, we take recent advances
in relevant areas into account and systematically devise eleven different
baseline solutions for multi-view deep OCC, which lays the foundation for
research on multi-view deep OCC. Third, to remedy the problem that limited
benchmark datasets are available for multi-view deep OCC, we extensively
collect existing public data and process them into more than 30 new multi-view
benchmark datasets via multiple means, so as to provide a publicly available
evaluation platform for multi-view deep OCC. Finally, by comprehensively
evaluating the devised solutions on benchmark datasets, we conduct a thorough
analysis on the effectiveness of the designed baselines, and hopefully provide
other researchers with beneficial guidance and insight to multi-view deep OCC.
Our data and codes are opened at https://github.com/liujiyuan13/MvDOCC-datasets
and https://github.com/liujiyuan13/MvDOCC-code respectively to facilitate
future research.
