近红外频谱的相似度测量方法与多属性信息相结合
Jinfeng Zhang1, Yuhua Qin1, Rongkun Tian1
1College of Information Science and Technology, Qingdao University of Science and Technology, China.
概括
一个新的St-SNE算法通过整合多属性信息,有效地减少近红外光谱数据的维度. 这种方法可以提高样本的区分和分类准确性,有助于产品配方设计.
科学领域:
- 化学测量 化学测量 化学测量
- 数据科学数据科学数据科学
- 机器学习 机器学习
背景情况:
- 近红外 (NIR) 频谱数据由于高维度,冗余性和非线性而带来了挑战.
- 样本属性,如生产面积和等级影响相似度,使数据分析复杂化.
- 现有的缩小维度的技术与复杂的光谱数据和属性集成作斗争.
研究的目的:
- 提出一种新的维度减小算法,Sinkhorn距离t分布式随机邻居嵌入 (St-SNE),用于分析复杂的NIR光谱数据.
- 将多属性信息与光谱数据集成在一起,以便更准确地评估样本相似性.
- 增强对产品配方设计等应用的分类和相似性搜索能力.
主要方法:
- 引入了Sinkhorn距离,以解决KL分歧不对称性和高维空间中的稀疏数据分布.
- 使用信息构建了一个多属性距离矩阵,并与混合数据矩阵的光谱数据相结合.
- 应用了St-SNE来减少维度,并将其性能与PCA,LPP,t-SNE,UMAP和Fisher t-SNE进行了比较.
主要成果:
- St-SNE有效地区分了具有不同属性信息的样本,在低维空间中显示了更清晰的投影边界.
- 与其他缩小维度方法相比,在烟草和果数据集上实现了最高的分类准确性.
- 在识别卷烟配方的类似烟草样本方面,与专家建议的超级一致性得到证明.
结论:
- St-SNE算法为高维,多属性光谱数据的维度减少提供了强大的解决方案.
- 它显著提高了样本歧视和分类准确性,优于现有方法.
- 通过增强相似性搜索和数据分析,St-SNE为产品配方维护和设计提供了宝贵的支持.
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