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Aim:
The aim of this workshop is to bring
together an interdisciplinary group of researchers from computer vision,
pattern recognition, and machine learning to present new component analysis
techniques and to identify the opportunities and challenges in applying
component analysis techniques to computer vision problems. The workshop
will be a mixture of invited talks and talks of previously unpublished
high-quality submitted papers.
Scope:
Linear and Multilinear methods (e.g. Principal
Component Analysis, Independent Component Analysis, Tensor factorization,
…) have been successfully applied to modeling, classification and
clustering in numerous visual, graphics and signal processing tasks over
the last four decades. Many learning/estimation problems in vision (e.g.
active appearance models, structure from motion, spectral clustering) can
be successfully solved using modifications of component analysis (CA)
methods. CA techniques are especially appealing because many can be solved
as generalized eigenvalue problems or alternated least squares procedures,
for which there exist extremely efficiently and numerically stable
algorithms. The main limitation of these approaches is that they are
usually optimal to find only linear/multilinear structure in the data.
However, in the late 90’s many researchers in the area of machine
learning, neural networks and statistics were able to cast many non-linear
problems for classification, clustering, visualization, dimensionality
reduction or modeling as a spectral decomposition of a kernel matrix. These
spectral approaches offer a potential for solving linear and non-linear
estimation/learning problems in vision efficiently and without local
minima.
The goal of this workshop is to discuss the state of
the art of component analysis algorithms for estimation and learning in
computer vision. Relevant topics of the workshop include (but are not
limited):
Advances in standard CA
techniques:
·
Principal
Component Analysis/Singular Value Decomposition, Linear Discriminant
Analysis, Canonical Correlation Analysis, Independent Component Analysis,
Partial least squares, Principal Component Regression, Correspondence
Analysis, Redundancy Analysis, Functional Component Analysis, …
·
Non-negative
matrix factorization
·
Spectral graph
methods
·
Kernel methods
·
Tensor decomposition
·
Multidimensional
scaling
·
Non-linear dimensionality reduction techniques (e.g. LLE, Isomap,
…)
·
Manifold learning
·
Latent variable
models
·
Inverse eigenvalue
problems
Applications of CA methods to computer vision problems:
·
Appearance Models
(active shape models, active appearance models, …)
·
Segmentation with
spectral graph methods (e.g. normalized Cuts)
·
Factorization
methods for rigid and non-rigid structure from motion
·
Object and face
recognition
·
Camera calibration
·
Robot localization
·
Feature selection
·
Low dimensional
visualization
·
Visual geometry
problems (e.g. trifocal tensor, fundamental matrix estimation, multiview
geometry)
Open research problems:
·
The role of
representation in CA methods. How to build representations invariant to
geometric transformations?.
·
How to learn an
optimal representation for clustering, classification,…?.
·
How do spectral
problems (with different normalizations) relate to the error functions and
least squares estimation problems?
·
Generative versus
discriminative learning with CA methods.
·
Learning from high
dimensional data and few training samples. How to build reliable estimates
of the components that generalize well from few training samples?
·
How to properly
normalize CA methods (e.g. how to normalize kernel matrices) ?
·
How do CA
techniques compare to state of the art techniques for classification (SVM,
adaboost), modeling (probabilistic graphical models), …
·
Optimal selection
of the number of components.
·
Non-linear
component analysis methods (beyond kernel methods). How to overcome local
minima problems.
·
Unified view of
classification, clustering, dimensionality reduction and modeling
algorithms.
·
How to learn
sparse feature representations.
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