nystrom
fun <T> nystrom(x: Array<T>, y: DoubleArray, t: Array<T>, kernel: MercerKernel<T>, noise: Double, normalize: Boolean = true): GaussianProcessRegression<T>
Fits an approximate Gaussian process model with Nystrom approximation of kernel matrix.
Parameters
x
the training dataset.
y
the response variable.
t
the inducing input, which are pre-selected or inducing samples acting as active set of regressors. In simple case, these can be chosen randomly from the training set or as the centers of k-means clustering.
kernel
the Mercer kernel.
noise
the noise variance, which also works as a regularization parameter.
normalize
the option to normalize the response variable.