数字稳定的局部保留部分最小平方的区分分析,以有效地减少维度和对高维数据的分类
1School of Mathematical Sciences, Universiti Sains Malaysia, 11800, Penang, Malaysia.
Heliyon
|February 26, 2024
概括
本研究引入了强大的局部性保存部分最小平方差分分析 (LPPLS-DA) 方法,以提高分类准确性. 这些技术提高了数值稳定性,以便在高维数据中更好地进行特征歧视.
科学领域:
- 机器学习 机器学习
- 数据科学数据科学数据科学
- 模式识别 模式识别
背景情况:
- 降低维度对于高维数据分类至关重要.
- 算法的数值稳定性直接影响了分类准确性.
- 高维数据往往导致分散矩阵奇点.
研究的目的:
- 探讨缩小维度和歧视子空间学习的数值属性.
- 建议强有力的实施地方性维护部分最小平方差异分析 (LPPLS-DA).
- 为了提高类别的分离性和分类准确性.
主要方法:
- 探索两个强大的LPPLS-DA实现.
- 优化数据预测,以改善特征歧视.
- 在合成和光谱数据集上的数值实验.
主要成果:
- 拟议的LPPLS-DA方法证明了更好的分类准确性.
- 实现了增强的特征歧视和优化数据预测.
- 与最先进的维度减小技术相比,性能优越.
结论:
- 强大的LPPLS-DA实现有效地解决了高维数据中的奇点问题.
- 提出的方法在分类和尺寸缩小方面提供了显著的改进.
- 这些发现凸显了数字稳定性在差别分析中的重要性.
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