Download presentation

Presentation is loading. Please wait.

Published byBailey Wentworth Modified over 5 years ago

8
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 }

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

OK

Topics in learning from high dimensional data and large scale machine learning Ata Kaban School of Computer Science University of Birmingham.

Topics in learning from high dimensional data and large scale machine learning Ata Kaban School of Computer Science University of Birmingham.

© 2018 SlidePlayer.com Inc.

All rights reserved.

To make this website work, we log user data and share it with processors. To use this website, you must agree to our Privacy Policy, including cookie policy.

Ads by Google