实验室关于流形学习在极限学习机中的应用研究被期刊Neurocomputing录用
新闻来源:IR实验室       发布时间:2019/10/12 10:11:15

  近日接到编辑部通知,博士生楚永贺的论文 "  Discriminative Globality-Locality Preserving Extreme Learning Machine for Image Classification" 被Neurocomputing录用。

摘要: 极限学习机(ELM)以其简单的理论和良好的泛化能力在分类任务中得到了广泛的应用。然而,目前对于ELM来讲,在保持数据的流形结构和数据蕴含的判别信息方面仍存在挑战。本文,我们提出了一种判别全局-局部保持极限学习机 (DGLELM). 与传统的极限学习机相比,DGELM 不仅考虑到数据样本的全局判别几何结构,同时考虑到了数据样本的局部判别几何结构。DGLELM通过最大化全局离散度和最小化全局类内离散度,同时最小化局部类内离散度和最大化局部类间散度,从而优化ELM输出权值的投影方向。为了验证所提算法的有效性,使用图像数据集进行实验。实验结果表明,DGLELM的分类结果表现出良好的性能。.

 

Abstract: Extreme learning machines (ELM) have been widely used in classification due to their simple theory and good generalization ability. However, there remains a major challenge: it is difficult for ELM algorithms to maintain the manifold structure and the discriminant information contained in the data. To address this issue, we propose a discriminant globality-locality preserving extreme learning machine (DGLELM) in this paper. In contrast to ELM, DGLELM not only considers the global discriminative structure of the dataset but also makes the best use of the local discriminative geometry information. DGLELM optimizes the projection direction of the ELM output weights by maximizing the inter-class dispersion and minimizing the intra-class dispersion for global and local data. Experiments on several widely used image databases validate the performance of DGLELM. The experimental results show that our approach achieves significant improvements over state-of-the-art ELM algorithms.