C-Loss-Based Doubly Regularized Extreme Learning Machine

Extreme learning machine has become a significant learning methodology due to its efficiency. However, extreme learning machine may lead to overfitting since it is highly sensitive to outliers. In this paper, a novel extreme learning machine called the C-loss-based doubly regularized extreme learning machine is presented to handle dimensionality reduction and overfitting problems. The proposed algorithm benefits from both L-1 norm and L-2 norm and replaces the square loss function with a C-loss function. And the C-loss-based doubly regularized extreme learning machine can complete the feature selection and the training processes simultaneously. Additionally, it can also decrease noise or irrelevant information of data to reduce dimensionality. To show the efficiency in dimension reduction, we test it on the Swiss Roll dataset and obtain high efficiency and stable performance. The experimental results on different types of artificial datasets and benchmark datasets show that the proposed method achieves much better regression results and faster training speed than other compared methods. Performance analysis also shows it significantly decreases the training time, solves the problem of overfitting, and improves generalization ability.