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segunda-feira, 23 de janeiro de 2012

Priberam Machine Learning Lunch Seminars

Priberam Machine Learning Lunch Seminar
Speaker: Miguel Almeida (IST/UTL and Aalto University, Finland)
Venue: IST Alameda, Sala PA2 (Edifício de Pós-Graduação)
Date: Tuesday, January 24th, 2012
Time: 13:00
Lunch will be provided

Title: SSS: Separation of Synchronous Sources


The problem of separating synchronous sources (SSS) is a case of blind
source separation (BSS) where independence of the sources is not
satisfied. In SSS, the sources are assumed to be complex-valued, and
different sources are phase-locked, which means that the relative
phase lag between two sources is not uniform in [0,2*pi[. For this
reason, the typical independent component analysis (ICA) tools are
theoretically not applicable, and experiments show that they perform
poorly in this task. In the SSS model, we assume that the phase lag
between any two sources is constant. The only important assumption
regarding the amplitudes of the sources is linear independence,
although some nice results can be proven if the amplitudes are
statistically independent.

In this talk, I'll start by briefly discussing ICA, since it is
relatively familiar in the Machine Learning community. I will then
formulate the problem of SSS and detail the similarities and
differences to the ICA problem. Afterwards, I will present two
algorithms that were developed to tackle this problem, along with some
nice theoretical properties of those algorithms. We will visit some
very simple optimization problems and a little bit of complex algebra.
Nothing complicated, I promise!

I will finalize by presenting some simulated results, on 1) data which
exactly follows the SSS model, and 2) data which deviates from the SSS


Bio: Miguel Almeida is currently a joint PhD student at IST-UTL, Portugal, and at Aalto University (AU), Finland (formerly Helsinki University of Technology), under joint supervision of Prof. José Bioucas-Dias (IST), Prof. Ricardo Vigário (AU), and Prof. Erkki Oja (AU). He started his doctoral project in 2008 and spent the first two years of his PhD at AU. He has been at IST since 2010, and plans to finish his degree in the first semester of 2012.
His PhD topic revolves around the SSS problem, and fits under the
general topic of Machine Learning. More specifically, this project
involves considerable amounts of Signal Processing and Optimization.
Miguel holds an MSc in Physics and Technology Engineering (IST, 2006)
and an advanced post-graduate degree in Biophysics (FC-UL, 2007).

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