The ICA 2007 Tutorials will take place on the afternoon of Sunday
9 September 2007.
Tutorials attendance is included in Regular and Student registration.
Tutorial 1 - Sparse Representations:
From Source Separation to Compressed Sensing
This tutorial will give an overview of the state of the art in the theory,
algorithms and applications of sparse signal representations. The main
emphasis will be on (blind) under-determined source separation, and
I will discuss some connections with the emerging field of compressed
sensing, which aims at exploiting sparse models to replace classical
Starting with the example of under-determined blind audio source separation,
I will illustrate intuitively the nature of sparse signal models and
how they can help solve ill-posed inverse problems. I will then survey
some recent mathematical results which analyze the properties of these
models and explain the performance of sparse representation algorithms
such as convex optimization methods and matching pursuit. I will conclude
by an introduction to compressed sensing with a discussion of its connections
to under-determined (blind) source separation, outlining the main theoretical
and practical challenges raised by these approaches.
Tutorial 2: Information Filtering
Jose C. Principe, Ph.D.
Distinguished Professor of Electrical and
University of Florida, Gainesville, USA
This tutorial will introduce a methodology
to train linear or nonlinear systems with entropy and divergence,
as opposed to the well known moment methods (e.g. mean square error
(MSE)). The advantage is that more information about the error
signal is captured in the weights of the mapper and a wealth of
new applications become possible, including independent component
analysis (ICA) as a special case. One of the corner stones of information
filtering is a methodology called information theoretic learning
(ITL) to estimate entropy directly from data, without estimating
probability density function explicitly. Applications to system
identification, blind deconvolution, independent component analysis,
principal curves and dimensionality reduction will be presented.
There is a very tight link between ITL and kernel methods being
developed now in the machine learning community. This tutorial
will also present a new similarity function called correntropy.
The name was coined to show that it is similar to correlation
but its mean value across delays (or dimensions) is the argument
of Renyi's quadratic entropy. This similarity function defines
a new reproducing kernel Hilbert space (RKHS) that shows a lot
of promise to design and implement nonlinear signal processing