低级别的表示与实证内核空间嵌入的多种形式的代表
Wenyi Feng1, Zhe Wang2, Ting Xiao2
1Information Technology Center, Qinghai University, Xining, 810016, PR China; Qinghai Provincial Laboratory for Intelligent Computing and Application, Xining, 810016, PR China.
概括
本研究介绍了低级别表示与实证内核空间嵌入的多元组 (LRR-EKM),一种无监督的方法,通过将数据投射到内核空间来提高分类准确性. LRR-EKM增强了分组表示和特征选择,以获得更好的性能.
科学领域:
- 机器学习 机器学习
- 数据科学数据科学数据科学
- 计算机视觉 计算机视觉
背景情况:
- 现有的低级别表示 (LRR) 方法由于对多重数据直接应用欧几里德算法而难以达到最佳分类准确性.
- 非线性数据结构往往阻碍了有效的低维多元体表示和分类.
研究的目的:
- 开发一种无监督的低级投影学习方法,提高对多重数据的分类准确性.
- 通过实现非线性数据的线性分离性来改善低维多元组的表示.
主要方法:
- 引入了低级别的表示与实证内核空间嵌入的多元组 (LRR-EKM).
- 使用实证内核映射将样本投射到重现的内核希尔伯特空间 (RKHS).
- 集成的行稀疏性和多重结构保留了投影矩阵上的约束.
主要成果:
- 与最先进的方法相比,LRR-EKM在各种现实数据集中实现了更高的性能.
- 该方法证明了改进的低维分组表示和增强的特征选择.
- 经验内核映射促进了非线性结构样本的线性分离性.
结论:
- LRR-EKM有效地解决了对多重数据的传统LRR方法的局限性.
- 拟议的方法提供了增强的分类准确性,可解释性,并保留了原始数据结构.
- 公开可用的代码有助于进一步的研究和应用.
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