由于高维,冗余,和近红外(NIR)光谱数据的非线性,以及样品的生产面积和品位等属性的影响,这些都会影响样本之间的相似性度量。结合多属性数据信息,提出了一种基于Sinkhorn距离的t分布随机近邻嵌入算法(St-SNE)。首先,引入了Sinkhorn距离,可以解决高维空间中KL发散不对称和数据分布稀疏等问题,从而构造使低维空间类似于高维空间的概率分布。此外,为了解决样本的多属性特征对相似性度量的影响,利用信息熵构造了多属性距离矩阵,然后结合光谱数据的数值矩阵得到混合数据矩阵。为了验证St-SNE算法的有效性,对近红外光谱数据进行降维投影,并与PCA进行比较,LPP,和t-SNE算法。结果表明,St-SNE算法能有效区分具有不同属性信息的样本,并在低维空间中产生了更清晰的样本类别投影边界。然后,我们使用烟草和芒果数据集测试了St-SNE对不同属性的分类性能,并将其与LPP进行比较,t-SNE,UMAP,和Fishert-SNE算法。结果表明,St-SNE算法对不同属性的分类准确率最高。最后,我们将搜索最相似样本的结果与卷烟配方的目标烟草进行了比较,实验表明,与其他算法相比,St-SNE与专家推荐的一致性最高。它可以为产品配方的维护和设计提供强有力的支持。
Due to the high-dimensionality, redundancy, and non-linearity of the near-infrared (NIR) spectra data, as well as the influence of attributes such as producing area and grade of the sample, which can all affect the similarity measure between samples. This paper proposed a t-distributed stochastic neighbor embedding algorithm based on Sinkhorn distance (St-SNE) combined with multi-attribute data information. Firstly, the Sinkhorn distance was introduced which can solve problems such as KL divergence asymmetry and sparse data distribution in high-dimensional space, thereby constructing probability distributions that make low-dimensional space similar to high-dimensional space. In addition, to address the impact of multi-attribute features of samples on similarity measure, a multi-attribute distance matrix was constructed using information entropy, and then combined with the numerical matrix of spectral data to obtain a mixed data matrix. In order to validate the effectiveness of the St-SNE algorithm, dimensionality reduction projection was performed on NIR spectral data and compared with PCA, LPP, and t-SNE algorithms. The results demonstrated that the St-SNE algorithm effectively distinguishes samples with different attribute information, and produced more distinct projection boundaries of sample category in low-dimensional space. Then we tested the classification performance of St-SNE for different attributes by using the tobacco and mango datasets, and compared it with LPP, t-SNE, UMAP, and Fisher t-SNE algorithms. The results showed that St-SNE algorithm had the highest classification accuracy for different attributes. Finally, we compared the results of searching the most similar sample with the target tobacco for cigarette formulas, and experiments showed that the St-SNE had the highest consistency with the recommendation of the experts than that of the other algorithms. It can provide strong support for the maintenance and design of the product formula.