View
217
Download
0
Category
Preview:
Citation preview
20 40 60 80 100 120 14020
40
60
80
100
120
140
20 40 60 80 100 120 14020
40
60
80
100
120
140
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 }
DRdR
0 10 20 30 40 50 60 70 80 90 10010
20
30
40
50
60
70
80
90
100
0 10 20 30 40 50 60 70 80 90 10010
20
30
40
50
60
70
80
90
100
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-
233454
11111122
11111222
11111212
12311222
12111222
11211223
11111333
11111333
11111122
11111122
11111122
11111222
11111222
11111223
11111333
11111333
input output
Definition: given two segmentations, A and B, the RI test will be:
When the function I{X} is an indicator function.
jiji jiji
jiji
BBandAA
orBBandAAIRI
, )]}()[(
)](){[(1
2
n
76.19%
86.67%
80.74%
87.55%
70.42%
64.13%
Recommended