Scholarlink

<p>As a classical dimensionality reduction method, linear discriminant analysis (LDA) is still a research hotspot. Although current researches on LDA improvement have achieved significant results, existing LDA algorithms still have limitations. Specifically, they focus on the dimensionality reduction of the original samples, without considering the correlation information among similar samples. To address this issue, this paper proposes a novel LDA mode—kernel transposed projection envelope LDA mode (KTPE-LDA-M). First, a transposed projection envelope transformation (TPET) algorithm is designed to extract correlation information among similar samples and enrich this information into the new generated samples—envelope samples. Then, to improve the efficiency of extracting nonlinear correlation information, the TPET is kernelized, resulting in the kernel transposed projection envelope transformation (KTPET) algorithm. Finally, the envelope samples generated by the KTPET are input into LDA, enabling dimensionality reduction based on the correlation information among similar samples. The experimental results demonstrate that the proposed mode can improve LDA’s performance apparently, with an average classification accuracy increase of 4.08%. These results indicate that the proposed mode is effective, and it is both necessary and advantageous to consider the correlation information among similar samples during LDA modeling.</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

Kernel Transposed Projection Envelope Linear Discriminant Analysis Mode

  • 期刊论文

  • 发表日期：

    2026年4月24日

查看全文
  • Yongming Li,
  • Wenqiang Zhao,
  • Fan Li,
  • Jie Ma,
  • Xiaoheng Zhang,
  • Pin Wang,
  • Yinghua Shen

摘要

As a classical dimensionality reduction method, linear discriminant analysis (LDA) is still a research hotspot. Although current researches on LDA improvement have achieved significant results, existing LDA algorithms still have limitations. Specifically, they focus on the dimensionality reduction of the original samples, without considering the correlation information among similar samples. To address this issue, this paper proposes a novel LDA mode—kernel transposed projection envelope LDA mode (KTPE-LDA-M). First, a transposed projection envelope transformation (TPET) algorithm is designed to extract correlation information among similar samples and enrich this information into the new generated samples—envelope samples. Then, to improve the efficiency of extracting nonlinear correlation information, the TPET is kernelized, resulting in the kernel transposed projection envelope transformation (KTPET) algorithm. Finally, the envelope samples generated by the KTPET are input into LDA, enabling dimensionality reduction based on the correlation information among similar samples. The experimental results demonstrate that the proposed mode can improve LDA’s performance apparently, with an average classification accuracy increase of 4.08%. These results indicate that the proposed mode is effective, and it is both necessary and advantageous to consider the correlation information among similar samples during LDA modeling.