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SVD and Signal Processing, III
book

SVD and Signal Processing, III

by M. Moonen, B. De Moor
March 1995
Intermediate to advanced content levelIntermediate to advanced
498 pages
23h 58m
English
Elsevier Science
Content preview from SVD and Signal Processing, III
338 L.P. Ammann
The weight matrix output by RSVD can be used to identify outliers in each principal
component as follows. Suppose that Z is a random variable from a standard Gaussian
distribution with mean 0 and s.d. 1. Let a denote some suitably small probability, e.g.,
a = .05, and let ua be the solution to
v(r < ~=)= ~.
Then any pixel with a weight less than or equal to ua in column k of the weight matrix
would be classified as an outlier in principal component k. For example, if r is the biweight
function with c = 4.685, a = .05, then ua = 0.6806, which would correspond ...
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Publisher Resources

ISBN: 9780444821072