Titlebook: Blind Source Separation; Advances in Theory, Ganesh R. Naik,Wenwu Wang Book 2014 Springer-Verlag Berlin Heidelberg 2014 Blind Source Separ

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Supervised Normalization of Large-Scale Omic Datasets Using Blind Source Separationr tens of thousands of molecular features (e.g., gene expression levels) in hundreds if not thousands of patient samples. A key statistical challenge in the analysis of such large omic datasets is the presence of confounding sources of variation, which are often either unknown or only known with err

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Blind Source Separation Based on Dictionary Learning: A Singularity-Aware Approachorithm (SparseBSS) to separate the noise corrupted mixed sources with very little extra information. We also discuss the singularity issue in the dictionary learning process, which is one major reason for algorithm failure. Finally, two approaches are presented to address the singularity issue.

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Exploratory Analysis of Brain with ICApplying ICA to evoked potentials (EPs) and event-related potentials (ERPs) is presented, as well as an explanation of the ICA of natural images and its relationship with models of visual cortex is also presented. This chapter is written as a general introduction to the subject for those who want to get started in the main topics.

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