TUTCRIS - Tampereen teknillinen yliopisto

TUTCRIS

Graph Embedded Extreme Learning Machine

Tutkimustuotosvertaisarvioitu

Yksityiskohdat

AlkuperäiskieliEnglanti
Sivut311 - 324
JulkaisuIEEE Transactions on Cybernetics
Vuosikerta46
Numero1
DOI - pysyväislinkit
TilaJulkaistu - 2016
Julkaistu ulkoisestiKyllä
OKM-julkaisutyyppiA1 Alkuperäisartikkeli

Tiivistelmä

In this paper, we propose a novel extension of the extreme learning machine (ELM) algorithm for single-hidden layer feedforward neural network training that is able to incorporate subspace learning (SL) criteria on the optimization process followed for the calculation of the network's output weights. The proposed graph embedded ELM (GEELM) algorithm is able to naturally exploit both intrinsic and penalty SL criteria that have been (or will be) designed under the graph embedding framework. In addition, we extend the proposed GEELM algorithm in order to be able to exploit SL criteria in arbitrary (even infinite) dimensional ELM spaces. We evaluate the proposed approach on eight standard classification problems and nine publicly available datasets designed for three problems related to human behavior analysis, i.e., the recognition of human face, facial expression, and activity. Experimental results denote the effectiveness of the proposed approach, since it outperforms other ELM-based classification schemes in all the cases.