Gaussian Pyramid for Nonlinear Support Vector Machine

Algorithm 1

Overview.

	Input: D: the training dataset (xi, yi), k: the size of the Gaussian pyramid must be an odd number
	Output: find an optimal hyperplane
	Start
	Step 1: data preprocessing on D
	Step 2: build the correlation matrix of D by equation:
	Step 3: randomly generate a Gaussian pyramid as a k × k matrix between 0 and 1
	Step 4: reduce the size of D using K to produce Dnew, a new training dataset, by equation:
	Step 5: find the multivariate normal distribution of Dnew by the following probability density function:
	Step 6: classify Dnew to find an optimal hyperplane and build the model to classify new data points.
	End