Multi-view Learning: Make Six Blinds Successful
Dacheng Tao
Professor
University of Technology, Sydney
Abstract: In recent years, many algorithms for learning from multi-view data by
considering the diversity of different views have been proposed. These
views may be obtained from multiple sources or different feature subsets.
For example, a person can be identified by face, fingerprint, signature or iris
with information obtained from multiple sources, while an image can be
represented by its color or texture features, which can be seen as different
feature subsets of the image. In this talk, we will organize the similarities
and differences between a wide variety of multi-view learning approaches,
highlight their limitations, and then demonstrate the basic fundamentals for
the success of multi-view learning. The thorough investigation on the view
insufficiency problem and the in-depth analysis on the influence of view
properties (consistence and complementarity) will be beneficial for the
continuous development of multi-view learning.
Bio: Dacheng Tao (F'15) is Professor of Computer Science with the Centre for
Quantum Computation & Intelligent Systems, and the Faculty of Engineering
and Information Technology in the University of Technology, Sydney. He
mainly applies statistics and mathematics to data analytics problems and his
research interests spread across computer vision, data science, image
processing, machine learning, and video surveillance. His research results
have expounded in one monograph and 200+ publications at prestigious
journals and prominent conferences, such as IEEE T-PAMI, T-NNLS, T-IP,
JMLR, IJCV, NIPS, ICML, CVPR, ICCV, ECCV, AISTATS, ICDM; and ACM SIGKDD,
with several best paper awards. He is a Fellow of the IEEE, OSA,
IAPR.