Local Gravitation Clustering-Based Semisupervised Online Sequential Extreme Learning Machine

Abstract

Due to the limited number of labeled samples, semisupervised learning often leads to a considerable empirical distribution mismatch between labeled samples and unlabeled samples. To this end, this paper proposes a novel semisupervised algorithm named Local Gravitation-based Semisupervised Online Sequential Extreme Learning Machine (LGS-OSELM), learning to unlabeled samples follows from easy to difficult. Each sample is formulated as an object with mass and associated with local gravitation generated from its neighbors. The similarity between samples is measurable by the local gravitation measures (centrality CE and coordination CO). First, the LGS-OSELM uses the labeled samples to learn the initialization model by implementing ELM. Second, the unlabeled samples with a high confidence level that is easy to learn are labeled with the pseudo label. Then, these samples are utilized to iterate the neural network by implementing OS-ELM. The proposed approach ultimately realizes effective learning of all samples through successive learning unlabeled samples and iterating neural networks. We implement experiments on several standard benchmark data sets to verify the performance of the proposed LGS-OSELM, which demonstrates that our proposed approach outperforms state-of-the-art methods in terms of accuracy.