April 2017
Intermediate to advanced
532 pages
12h 39m
English
book
Machine Learning with Spark - Second Edition
by Rajdeep Dua, Brian O'Neill, Stephen Boesch, Manpreet Singh Ghotra, Nick Pentreath
Content preview from Machine Learning with Spark - Second Edition
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Running PCA on the LFW dataset
Now that we have extracted our image pixel data into vectors, we can instantiate a new RowMatrix.
def computePrincipalComponents(k: Int): Matrix
Computes the top k principal components. Rows correspond to observations, and columns correspond to variables. The principal components are stored as a local matrix of size n-by-k. Each column corresponds for one principal component, and the columns are in descending order of component variance. The row data do not need to be "centered" first; it is not necessary for the mean of each column to be 0.
Note that this cannot be computed on matrices with more than 65535 columns.
K is the number of top principal components. It returns a matrix of size n-by-k, whose columns ...
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