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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Presentation transcript:

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 }

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).

PCA +(x,y)

inputoutput

Definition: given two segmentations, A and B, the RI test will be: When the function I{X} is an indicator function.

76.19% 86.67%

80.74% 87.55%

70.42% 64.13%