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Research on Embedding Unsupervised Learning and Application on Tobacco Leaf Data

机译：烟草叶数据嵌入无监督学习与应用的研究

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Data-driven is the core of distance measurement, which is used to measure and classify the similarity of tobacco leaf producing areas and improve the accuracy of clustering. The article proposes a spectral clustering algorithm based on auto encoding called AESC, which consists of two parts: auto encoder and spectral clustering layer. The auto encoder is used for feature learning to improve the accuracy of distance construction, and the spectral clustering layer makes the algorithm more adaptable to data distribution. After experiments, the autoencoding spectral clustering algorithm proposed in this paper is higher than the traditional Kmeans, DBscan and spectral clustering algorithms in the Silhouette Coefficient. And the similarity measurement results are mutually verified with research in the tobacco industry, which proves the reliability of the algorithm for the similarity measurement of tobacco leaf quality and is of great significance for improving the stability of cigarette quality.
机译：数据驱动是距离测量的核心，用于测量和分类烟草叶片产生区域的相似性并提高聚类的准确性。本文提出了一种基于自动编码的频谱聚类算法，该算法由AESESESESESESC组成，包括两个部分：自动编码器和频谱聚类层。自动编码器用于特征学习以提高距离结构的准确性，并且光谱聚类层使算法更适应数据分布。实验之后，本文提出的自动编码谱聚类算法高于传统的浏览器，DBSCAN和轮廓系数的光谱聚类算法。相似性测量结果在烟草行业的研究中相互验证，证明了烟草叶片质量相似度测量的可靠性，对提高卷烟质量的稳定性具有重要意义。

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