Scholarlink

<p>Multi-metric learning techniques are designed to enhance optimization processes, such as imputing missing data in high-dimensional, multimodal datasets, where multiple local distance functions are defined. Here, we first outline the theoretical foundations of multi-distance metric learning and relevant concepts from group theory. Convergence results, followed by experimental evaluations on the LINCS dataset, including comparisons with two other methods are presented. Finally, an ablation study to illustrate how incorporating each additional metric contributes to improved overall performance is provided.</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

Algebraic formulation of simultaneous tensor-metric learning

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月25日

查看全文
  • Maryam Bagherian

摘要

Multi-metric learning techniques are designed to enhance optimization processes, such as imputing missing data in high-dimensional, multimodal datasets, where multiple local distance functions are defined. Here, we first outline the theoretical foundations of multi-distance metric learning and relevant concepts from group theory. Convergence results, followed by experimental evaluations on the LINCS dataset, including comparisons with two other methods are presented. Finally, an ablation study to illustrate how incorporating each additional metric contributes to improved overall performance is provided.