Fast and robust fixed-point algorithms for independent component analysis
IEEE Transactions on Neural Networks
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High dimensionality, missing values, noise, and outliers are standard problems in gene expression data and are usually dealt with separately. In this paper, we propose an ideal point model that performs feature extraction, imputes missing values, and is robust to noise and outliers in a unified and unsupervised framework. We use the simplifying assumption that genes are either expressed or not expressed in order to obtain a parsimonious model. We present a fast Bayesian method for estimating the large number of parameters in the ideal point model. We apply the ideal point model to a leukemia data set, where it outperforms independent component analysis (ICA), a state of the art unsupervised feature extraction method.