 Dr. Feng Tian
Professor, Bournemouth
University, UK
Dr Feng Tian is currently a professor in Bournemouth University, UK. With expertise
on digital media, image processing and machine learning, Dr Tian has published
over 100 papers or book chapters in peer-reviewed journals or international
conferences, including IEEE Transactions on
Visualization and Computer Graphics, ACM Transactions on Modelling
and Computer Simulation, IEEE Transactions on Cybernetics,
Visual Computer, Computer & Graphics, Multimedia Tools & Applications,
International Joint Conference on Artificial Intelligence (IJCAI), Association
for the Advancement of Artificial Intelligence (AAAI), Pacific Graphics (PG),
IJCNN, CASA, CGI, etc. Before coming to the UK, Dr Tian worked as a
post-doctoral fellow and assistant professor in Nanyang Technological
University, Singapore. Dr Tian has also been awarded with research grants from
Singapore National Research Foundation (Singapore), Royal Society (UK), British
Art Council (UK), Horizon 2020 (EU), etc.
A good data representation can typically reveal the latent structure of
data and facilitate further processes such as clustering, classification and
recognition. Nonnegative matrix factorization (NMF) as a fundamental approach
for data representation has attracted great attentions. Despite its great
performance, traditional NMF fails to explore the semantic information of
multiple components as well as the diversity among them, which would be of
great benefit to understand data comprehensively and in depth. In fact, real
data are usually complex and contain various components. For example, face
images have ex-pressions and genders. Each component mainly reflects one aspect
of data and provides information others do not have. In this talk, I will
present an approach on multi-component nonnegative matrix factorization
(MCNMF). Instead of seeking only one representation of data, MCNMF learns
multiple representations simultaneously, where each representation corresponds
to a component. By integrating the multiple representations, a more
comprehensive representation is then established.
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Invited Talk >