Incremental non-gaussian analysis of microarray gene expression data

The microarray is gaining popularity in biomedical research due to its ability to analyze hundreds to thousands of genes simultaneously in one experiment. However, the unique nature of microarray data, with a large number of features but relative small number of samples, poses challenges to process the microarray data effectively. The curse of dimensionality introduces the importance of feature extraction in analyzing microarray data. Therefore, we propose a novel incremental method to discover the non-Gaussian weight from the microarray gene expression data with high efficiency. Our proposed method can discover a small number of compact features from a huge number of genes and can still achieve good predictive performance. It integrates non-gaussianity and an adaptive incremental model in an unsupervised way to extract informative features. It is also plausible to analyze microarray data with the number of features much larger than number of observations with promising results.