Instructor Activity Recognition through Deep Spatiotemporal Features and Feedforward Extreme Learning Machines

Algorithm 1

The ELM algorithm for instructor activity recognition.

Input: Deep spatiotemporal features x, target label T, number of hidden nodes L,

activation function G. Let, w be the weight between ELM input layer and hidden

layer, b is biased vector, β is output weights, G is the ELM activation function, Y is

the predicted output vector, H output matrix of hidden layer, is the generalized

Moore-Penrose inverse matrix.

Output: parameters of ELM, w, b, β, and prediction response Y.

Generate randomly w and b

Compute H=G(xw + b)

Computeβ=H^†T.

Compute Y=H β

Return w, b, β, Y