Principal Component Analysis

# Principal Component Analysis

Computes the top k principal component coefficients for the m-by-n data matrix X. Rows of X correspond to observations and columns correspond to variables. The coefficient matrix is n-by-k. Each column of the coefficients return matrix contains coefficients for one principal component, and the columns are in descending order of component variance. This function centers the data and uses the singular value decomposition (SVD) algorithm, so that future optimizations to SVD also boost performance of this PCA implementation.

## Testing
Tests included:
 * All principal components
 * Only top k principal components

The results are tested against MATLAB's pca: http://www.mathworks.com/help/stats/pca.html

## Documentation
Added to mllib-guide.md

## Example Usage 
Added to examples directory under SparkPCA.scala