Independent component analysis: algorithms and applications
Neural Networks
Incremental PCA or On-Line Visual Learning and Recognition
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
Independent Component Analysis: A Tutorial Introduction
Independent Component Analysis: A Tutorial Introduction
Introduction to Bioinformatics
Introduction to Bioinformatics
Discriminatory mining of gene expression microarray data
Journal of VLSI Signal Processing Systems - Special issue on signal processing and neural networks for bioinformatics
Streaming pattern discovery in multiple time-series
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Gene expression time series modeling with principal component and neural network
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Incremental Hybrid Approach for Microarray Classification
ICMLA '08 Proceedings of the 2008 Seventh International Conference on Machine Learning and Applications
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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.