1: Input the training set
\left\{{\left\{{x_{it}^\nu,P_{it}^\nu} \right\}_{i = 1}^N} \right\}_{t = 1}^M
; |
2: Set the convergence criterion for ω by using the difference between the current estimation and the next estimation; |
3: Set η = 1 and the maximum iteration number to be ηmax = 50; |
4: Initialize the parameter ω; |
5: Initialize the threshold value ωth; |
6: Initialize the RVs matrix by setting PRV = P; |
7: while Maximum iteration or convergence criteria is reached do |
8: Creating the kernel matrix according to (6); |
9: Calculate the inverse covariance matrix of ω according to (26); |
10: Calculate the mean vector according to (25); |
11: Updating the hyper-paramter as
{\bf{\lambda}}_i^{\left({\eta + 1} \right)} = {{1 - {\bf{\lambda}}_i^{\left(\eta \right)}{\bf{\Lambda}}_i^{- 1}} \over {{\bf{\mu}}_i^2}}
;
|
12: Eliminate the ωi and the samples Pi with ωi > ωth; |
13: Updating kernel matrix by using the eliminated samples; |
14: end while |
15: Output the estimation of ω and λ |