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Application of Adaptive Principal Component Extraction to Gene Expression Data

Author: YangShengQi
Tutor: ZhangJunYing
School: Xi'an University of Electronic Science and Technology
Course: Applied Computer Technology
Keywords: Feature Extraction Principal Component Analysis Adaptive principal component extraction algorithm Artificial Neural Networks Gene expression data
CLC: TP399-C8
Type: Master's thesis
Year: 2008
Downloads: 34
Quote: 0
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Abstract


Has been the principal component analysis (PCA) is a highly scientific research scholars pay close attention , it can find a reversible orthogonal transformation , the original high -dimensional data is projected onto the lower-dimensional data space , and retain the main features of the data . PCA has been widely used in many fields of modern signal processing , such as data compression , feature extraction , pattern recognition , digital communications , and computer vision . However , the traditional PCA method based on KL transform data autocorrelation matrix eigenvalues ??and vector calculation , but such a large number of matrix operations programmed some difficulty , increase the complexity of the algorithm . This paper will combine the PCA method and artificial neural network , a multi-master extraction algorithm based on neural network , focusing on adaptive principal component extraction (APEX) neural network algorithm . APEX algorithm to take advantage of unsupervised adaptive neural network parallel computing direct extraction of principal components , thus greatly improving the speed of feature extraction . This article uses a Gaussian distribution of artificial simulation data and high- wiki gene expression data , the the APEX algorithm with traditional PCA algorithm based on KL transform various aspects of performance comparison . And for the first time the APEX neural network algorithm for gene expression data processing , through comparative experiments on the Yeast and NCI64 two kinds of gene expression data show that the the APEX algorithm and the traditional principal component extraction algorithm based on KL transform compared with significantly faster operation speed. In addition, according to the principal components of the original gene expression data reconstruction, Expand pave the way for the next step in the subject .

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