通过联合l2,1和核规范最小化支持矩阵机器,在矩阵完成框架下对腐败数据的分类进行核规范最小化
IEEE transactions on neural networks and learning systems
|August 1, 2023
概括
支持矩阵机 (SSMRe) 通过同时执行矩阵恢复和特征选择,提供强大的高维数据分类. 这种方法有效地处理损坏的数据和异常值,提高复杂数据集的分类准确性.
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:
- 传统的支持矢量机器 (SVM) 容易受到异常值的影响,在具有损坏列的高维数据集中降低性能.
- 当数据特征中只有一小部分受损时,分类准确性会显著下降.
研究的目的:
- 提出一个高效的支持矩阵机 (SSMRe) 用于高维数据分类在任意损坏列的存在.
- 开发一种同时执行矩阵恢复 (特征选择) 和分类的方法.
主要方法:
- 引入了同时执行矩阵恢复 (SSMRe) 的支持矩阵机器.
- 用于特征选择和分类的l2,1规范 (L的核规范) 的联合最小化.
- 假设数据包括一个低级清洁矩阵加上一个稀疏的噪音矩阵,利用弹性网的光谱延伸.
主要成果:
- 在不连贯和模两可的条件下,SSMRe证明了有效的矩阵恢复和分类.
- 该方法成功地恢复了更高等级的内在矩阵,即使有密集的损坏数据.
- 在BCI,面部识别和人身识别数据集上观察到显著的性能增长,特别是在异常存在的情况下.
结论:
- SSMRe提供了一个强大的解决方案,用于高维分类与损坏的数据和异常值.
- 拟议的方法结合了矩阵恢复,低级别属性和复杂的噪音数据的联合稀疏性.
- 在保持合理数量的支持矢量的同时,SSMRe在现实应用中取得了显著的改进.
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