An accelerating scheme for destructive parsimonious extreme learning machine
Constructive and destructive parsimonious extreme learning machines (CP-ELM and DP-ELM) were recently proposed to sparsify ELM. In comparison with CP-ELM, DP-ELM owns the advantage in the number of hidden nodes, but it loses the edge with respect to the training time. Hence, in this paper an equivalent measure is proposed to accelerate DP-ELM (ADP-ELM). As a result, ADP-ELM not only keeps the same hidden nodes as DP-ELM but also needs less training time than CP-ELM, which is especially important for the training time sensitive scenarios. The similar idea is extended to regularized ELM (RELM), yielding ADP-RELM. ADP-RELM accelerates the training process of DP-RELM further, and it works better than CP-RELM in terms of the number of hidden nodes and the training time. In addition, the computational complexity of the proposed accelerating scheme is analyzed in theory. From reported results on ten benchmark data sets, the effectiveness and usefulness of the proposed accelerating scheme in this paper is confirmed experimentally.
extreme learning machine single-hidden layer feedforward network destructive algorithm constructive algorithms sparseness