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Independent Principal Component Analysis for biologically meaningful dimension reduction of large biological data sets

✍ Scribed by Yao, Fangzhou (author);Coquery, Jeff (author);Lê Cao, Kim Anh (author)

Book ID: 119894237
Publisher: BioMed Central
Year: 2012
Tongue: English
Weight: 412 KB
Volume: 13
Category: Article
ISSN: 1471-2105
DOI: 10.1186/1471-2105-13-24

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In 1901, Karl Pearson invented Principal Component Analysis (PCA). Since then, PCA serves as a prototype for many other tools of data analysis, visualization and dimension reduction: Independent Component Analysis (ICA), Multidimensional Scaling (MDS), Nonlinear PCA (NLPCA), Self Organizing Maps (SO