1. |
//Set the primary value of the probability mathematical model of data filtering |
2. |
X (1) = X, Y (1) = Y, N (1) = n, i = 1 |
3. |
whileN (i)〉L |
4. |
//Calculate data characteristics positioning matrix to get a fuzzy decision power index p(i) |
5. |
if N (i) mod L〈m |
6. |
then p(i) = N (i)/L |
7. |
else |
8. |
p(i) = N (i)/L + 1 |
9. |
//With an independent main component analysis method, the fuzzy decision and probability statistics of data filtering are obtained, and the fuzzy identification matrix of data filtration output is obtained X (i) and Y (i). |
10. |
Xij = X (i) [(j − 1 + 1 : L* j)] |
11. |
Yij = Y (i) [L* (J − 1) + 1 : L* j] |
12. |
//use SVD breakout operation method, power index statistics, execute the following cycle. |
13. |
for j = 1 to p(i) |
14. |
{X_{ij}} = {U_{ij}}\sum\nolimits_{ij} V_{ij}^T
|
15. |
End |
16. |
Feature statistic for data filtering output X (i + 1), Y (i + 1) |
17. |
i = i + 1 |
18. |
//End of iteration, realize the statistical regression analysis of large data filtration. |
19. |
k = i − 1 |
20. |
X (k) = U (k)∑(k)V(k)T |
21. |
Seeking the main feature β*, determine whether the convergence condition is met, if not satisfied, go to Step 10. |
22. |
If the convergence condition is met, the algorithm ends. |