Connectionists: Riemannian Geometry workshop @ NIPS2014

[Vittorio Murino]

Call for Participation

RIEMANNIAN GEOMETRY IN MACHINE LEARNING, STATISTICS, AND COMPUTER VISION

This workshop will be held in conjunction with Neural Information 
Processing Systems (NIPS 2014), on Saturday December 13 2014, at the 
Palais des Congrès in Montreal, Canada.

http://www.riemanniangeometry2014.eu/


OVERVIEW
Traditional machine learning and data analysis methods often assume that 
the input data can be represented by vectors in Euclidean space. While 
this assumption has worked well for many applications, researchers have 
increasingly realized that if the data is intrinsically non-Euclidean, 
ignoring this geometrical structure can lead to suboptimal results.
In the existing literature, there are two common approaches for 
exploiting data geometry when the data is assumed to lie on a Riemannian 
manifold.

In the first direction, often referred to as manifold learning, the data 
is assumed to lie on an unknown Riemannian manifold and the structure of 
this manifold is exploited through the training data, either labeled or 
unlabeled. Examples of manifold learning techniques include Manifold 
Regularization via the graph Laplacian, Locally Linear Embedding, and 
Isometric Mapping.
In the second direction, which is gaining increasing importance and 
success, the Riemannian manifold representing the input data is assumed 
to be known explicitly. Some manifolds that have been widely used for 
data representation are: the manifold of symmetric, positive definite 
matrices, the Grassmannian manifold of subspaces of a vector space, and 
the Kendall manifold of shapes. When the manifold is known, the full 
power of the mathematical theory of Riemannian geometry can be exploited 
in both the formulation of algorithms as well as their theoretical analysis.
Successful applications of these approaches are numerous and range from 
brain imaging and low rank matrix completion to computer vision tasks 
such as object detection and tracking.

This workshop focuses on the latter direction. We aim to bring together 
researchers in statistics, machine learning, computer vision, and other 
areas, to discuss and exchange current state of the art results, both 
theoretically and computationally, and identify potential future 
research directions.


INVITED SPEAKERS
Thomas Fletcher, University of Utah
Mark Girolami, University of Warwick
Richard Hartley/Mehrtash Harandi, Australian National University/NICTA
Suvrit Sra, Max Planck Institute for Intelligent Systems, Tuebingen
Anuj Srivastava, Florida State University
Bart Vandereycken, Princeton University


FORMAT
The workshop will consist of invited talks only, followed by a panel 
discussion together with the speakers.


ORGANIZING COMMITTEE
Minh Ha Quang (Istituto Italiano di Tecnologia, Genova, Italy)
Vikas Sindhwani (IBM T.J. Watson Research Center, New York)
Vittorio Murino (Istituto Italiano di Tecnologia, Genova, and University 
of Verona, Italy)


-- 
Vittorio Murino

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Prof. Vittorio Murino, Ph.D.
PAVIS - Pattern Analysis & Computer Vision

IIT Istituto Italiano di Tecnologia
Via Morego 30
16163 Genova, Italy

Phone:	+39 010 71781 504
Mobile: +39 329 6508554
Fax: +39 010 71781 236
E-mail: vittorio.murino at iit.it

Secretary: Sara Curreli
email:	sara.curreli at iit.it
Phone: 	+39 010 71781 917

http://www.iit.it/pavis
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