Scholarlink

Abstract <p>To tackle the issues of incomplete feature reconstruction and insufficient feature representation in the traditional extreme learning machine autoencoder (ELM-AE), this paper proposes a multilayer GEELM-AE architecture based on cyclic structure (GEELM-AE-MCS). First, we embed the weights into the reconstruction error function to enhance local feature clustering by integrating graph embedding theory. Second, we incorporate the graph embedding matrix into the ELM feature space to preserve both global structural information and similarity of the feature data, thereby enabling the algorithm to establish a more effective boundary for feature discrimination. Finally, we propose GEELM-AE-MCS, which leverages each self-encoder’s dimensionality reduction capability to further enhance algorithm performance. Experimental results demonstrate that GEELM-AE-MCS exhibits superior feature representation and classification capabilities compared to state-of-the-art algorithms.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Graph Embedded Extreme Learning Machine Autoencoder with Multilayer Cyclic Structure

  • 期刊论文

  • 发表日期：

    2025年8月1日

查看全文
  • Di Wu,
  • ZiHan Chen

摘要

Abstract

To tackle the issues of incomplete feature reconstruction and insufficient feature representation in the traditional extreme learning machine autoencoder (ELM-AE), this paper proposes a multilayer GEELM-AE architecture based on cyclic structure (GEELM-AE-MCS). First, we embed the weights into the reconstruction error function to enhance local feature clustering by integrating graph embedding theory. Second, we incorporate the graph embedding matrix into the ELM feature space to preserve both global structural information and similarity of the feature data, thereby enabling the algorithm to establish a more effective boundary for feature discrimination. Finally, we propose GEELM-AE-MCS, which leverages each self-encoder’s dimensionality reduction capability to further enhance algorithm performance. Experimental results demonstrate that GEELM-AE-MCS exhibits superior feature representation and classification capabilities compared to state-of-the-art algorithms.