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Current Trends in Bayesian Methodology with Applications
book

Current Trends in Bayesian Methodology with Applications

by Satyanshu K. Upadhyay, Umesh Singh, Dipak K. Dey, Appaia Loganathan
May 2015
Intermediate to advanced content levelIntermediate to advanced
680 pages
22h 33m
English
Chapman and Hall/CRC
Content preview from Current Trends in Bayesian Methodology with Applications
Scalable Subspace Clustering with Application to Motion Segmentation 277
13.4.1 Dimension reduction
Dimension reduction is an essential pr e processing step for obtaining a g ood
motion segmentation. To realize this goal, the truncated SVD is often ap-
plied [5, 12, 17, 24].
To project the measurement matrix W R
2F ×N
to X = [x
1
, ..., x
N
]
R
D×N
, where D is the desired projection dimension, the matrix W is decom-
posed via SVD as W = UΣV
T
and the first D columns of the matrix V are
chosen a s X
T
.
The value of D for dimension reduction is also a major concern in motion
segmentation. This value has a large impact on the speed and acc uracy of the
final result, so it is very important to select the best dimension to perform
the segmentation. The dimension
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Publisher Resources

ISBN: 9781482235128