Quasi-Supervised Learning for Biomedical Data Analysis
We present a novel formulation for pattern recognition in biomedical data. We adopt a binary recognition scenario where a control dataset contains samples of one class only, while a mixed dataset contains an unlabeled collection of samples from both classes. The mixed dataset samples that belong to the second class are identified by estimating posterior probabilities of samples for being in the control or the mixed datasets. Experiments on synthetic data established a better detection performance against possible alternatives. The fitness of the method in biomedical data analysis was further demonstrated on real multi-color flow cytometry and multi-channel electroencephalography data.
You can access the full-length manuscript at the Pattern Recognition web site (doi:10.1016/j.patcog.2010.04.024).
Financial support was provided by the 7th Framework Programme, Marie Curie Actions - International Re-integration Grants (PIRG03-GA-2008-230903).
The Matlab implementation of the quasi-supervised learning algorithm is provided free of charge for non-commercial academic use. The package is Copyright (C) 2010-2019 by Bilge Karaçalı.
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