This algorithm is used for dimension reduction. Input: a set of vectors {Xn є }, and dimension d,d
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Transcript of This algorithm is used for dimension reduction. Input: a set of vectors {Xn є }, and dimension d,d
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This algorithm is used for dimension reduction. Input: a set of vectors {Xn є }, and dimension d ,d<D.Output: a set of vectors {Yn є }
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This Iterative algorithm is used for grouping of vectors. Input: a set of vectors {Xn є D}, number of groups-P.Output: a set of vectors {Xn є D}, which are labeled by (1…P).
This Iterative algorithm offers a statistical model for a set of vectors.Input: a set of vectors {Xn є D}, number of groups-P, expectations of
each group, empiric probability, empiric variances.Output: a set of vectors {Xn є D}, which are labeled by (1…P).
834.7348250.3190-31.1367-27.5612-61.657078.9281-
PCA(+x,y)
834.7348250.3190-31.1367-27.5612-61.657078.9281-
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input output
Definition: given two segmentations, A and B, the RI test will be:
When the function I{X} is an indicator function.
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76.19%
86.67%
80.74%
87.55%
70.42%
64.13%