| 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. |
