Local Gravitation Clustering-Based Semisupervised Online Sequential Extreme Learning Machine

Algorithm 1

The proposed LGS-OSELM algorithm.

	input. Labeled samples set , unlabeled samples set , and activation function, .
	Output: The decision functions: f (x) = .
	Use to build the initial ELM model by implementing ELM. That is, according to equation (6), calculate
	A temporary sample set is established.
	while is not empty do
	For each unlabeled samples , calculate its relative CEs and
	then find .
	Ifthen
	x^u is removed into Z from set .
	else
	Calculate
	if < 0 then
	x^u is removed into Z from set
	else
	For each unlabeled sample , calculate all its relative LRFs, and then find ,
	x^u is removed into Z from set .
	end if
	end if
	Unlabeled samples in Z are inserted into the ELM model to generate pseudo labels. Remove the samples into from Z.
	Update the network model with the result of the labeled samples. That is, update and P based on equations (7) and (8).
	end while