Download presentation

Presentation is loading. Please wait.

Published byBailey Wentworth Modified over 3 years ago

8
This algorithm is used for dimension reduction. Input: a set of vectors {Xn є }, and dimension d,d

9
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).

10
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).

13
834.7348 250.3190- 31.1367- 27.5612- 61.6570 78.9281- PCA +(x,y) 834.7348 250.3190- 31.1367- 27.5612- 61.6570 78.9281- 233 454

19
22111111 22211111 21211111 22211321 22211121 32211211 33311111 33311111 22111111 22111111 22111111 22211111 22211111 32211111 33311111 33311111 inputoutput

31
Definition: given two segmentations, A and B, the RI test will be: When the function I{X} is an indicator function.

32
76.19% 86.67%

33
80.74% 87.55%

34
70.42% 64.13%

Similar presentations

© 2017 SlidePlayer.com Inc.

All rights reserved.

Ads by Google