This paper proposes a new method to train stable extreme learning machines (ELM). The new method, called StaELM, uses corre- lation coefficients in Gaussian process to measure the similarities between different hidden layer outputs. Different from kernel operations such as linear or RBF kernels to handle hidden layer outputs, using correlation coefficients can quantify the similarity of hidden layer outputs with real numbers in (0, 1) and avoid covariance matrix in Gaussian process to become a singular matrix. Training through Gaussian process results in ELM models insensitive to random initialization and can avoid over- fitting. We analyse the rationality of StaELM and show that existing kernel-based ELMs are special cases of StaELM. We used real world datasets to train both regression and classification StaELM models. The experiment results have shown that StaELM models achieved higher accuracies in both regression and classification in comparison with tra- ditional kernel-based ELMs. The StaELM models are more stable with respect to different random initializations and less over-fitting. The train- ing process of StaELM models is also faster.
['Yulin He', 'Joshua Zhexue Huang', 'Xizhao Wang', 'Rana Aamir Raza']
Use Correlation Coefficients in Gaussian Process to Train Stable ELM Models