Globality‐Locality Preserving Maximum Variance Extreme Learning Machine

Abstract

An extreme learning machine (ELM) is a useful technique for machine learning; however, the existing extreme learning machine methods cannot exploit the geometric structure information or discriminate information of the data space well. Therefore, we propose a globality-locality preserving maximum variance extreme learning machine (GLELM) based on manifold learning. Based on the characteristics of the traditional ELM method, GLELM introduces the basic principles of linear discriminant analysis (LDA) and local preservation projection (LPP) into ELM, fully taking account of the discriminant information contained in the sample. This method can preserve the global and local manifold structures of data to optimize the projection direction of the classifier. Experiments on several widely used image databases and UCI datasets validate the performance of GLELM. The experimental results show that the proposed model achieves promising results compared to several state-of-the-art ELM algorithms.

A novel indicator weight tuning method based on fuzzy theory in mobile social networks

“…And the variance statistically reflects differences in the level of an important indicator. Based on the idea of maximum variance, a set of weights should make the corresponding evaluation results reached the maximum total variance [38,39], so that the evaluation results in the overall coverage are more reasonable for actual situation, as shown in Figure 6.…”