IntroductionThis is the first data analysis and signal processing competition that is being organized in conjunction with the IEEE Machine Learning for Signal Processing Workshops. This competition is envisioned to become an annual event where problems relevant to the mission and interests of the MLSP community will be presented with the goal of advancing the current state-of-the-art in both theoretical and practical aspects. The problems are selected to reflect the current trends to evaluate existing approaches on common benchmarks as well as areas where crucial developments are thought to be necessary. If you would like to contribute a problem for next year’s MLSP competition please contact the Competition Chair (Deniz Erdogmus, email@example.com).
Benchmark ProblemsThis year’s competition consists of three problems. All three problems are of general interest to the signal processing community and bear strong importance and relevance to the advancement of the current state-of-the-art in the application of advanced adaptation and machine learning concepts to signal processing problems.
- Contribution by Andrzej Cichocki: Evaluation and development of blind source separation algorithms in linear instantaneous mixtures for large-scale (high-dimensional), ill-conditioned, and noisy measurements.
- Contribution by Vince Calhoun: Evaluation and development of intelligent artifact rejection in multidimensional signal processing, posed as a problem of eye-artifact removal in the context of event-related potential detection from EEG measurements.
- Contribution by Kenneth Hild: Evaluation and development of noise reduction algorithms and methodologies, posed as a problem of signal denoising to reduce number of required trials for MEG measurements.
The winners of the 2005 Data Analysis of Competition:
- Problem 1
- ICA Benchmark: S.C. Douglas, J. Chao
- Problem 2
- Artifact Reduction in Multichannel EEG: N. Nicolaou, S. Nasuto
- Problem 3
- Single-trial MEG Denoising: M. Grosse-Wentrup and M. Buss
Good Luck, MLSP Organization Committee