Principal component analysis (PCA) is a dimensionality reduction tool which captures the features of data set in low dimensional subspace. Traditional PCA uses L2-PCA and has much desired orthogonality properties, but is sensitive to outliers. PCA using L1 norm has been proposed as an alternative to counter the effect of outliers. The R environment for statistical computing already provides L2-PCA function prcomp(), but there are not many options for L1 norm PCA methods. The goal of the research was to create one R package with different options of PCA methods using L1 norm. So, we choose three different L1-PCA algorithms: PCA-L1 proposed by Kwak [10], L1-PCA* by Brooks et. al. [1], and L1-PCA by Ke and Kanade [9]; to create a package pcaL1 in R, interfacing with C implementation of these algorithms. An open source software for solving linear problems, CLP, is used to solve the optimization problems for L1-PCA* and L1-PCA. We use this package on human microbiome data to investigate the relationship between people based on colonizing bacteria.
| Identifer | oai:union.ndltd.org:vcu.edu/oai:scholarscompass.vcu.edu:etd-3487 |
| Date | 03 May 2011 |
| Creators | Jot, Sapan |
| Publisher | VCU Scholars Compass |
| Source Sets | Virginia Commonwealth University |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Theses and Dissertations |
| Rights | © The Author |
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