分析测试学报2024，Vol.43Issue(7)：1039-1045,7.DOI:10.12452/j.fxcsxb.24032104
基于PCA+KNN和kernal-PCA+KNN算法的废旧纺织物鉴别
Identification of Waste Textiles Based on PCA+KNN and kernel-PCA+KNN Algorithms
摘要
Abstract
The study collected 4 998 near infrared spectra of 15 types of waste textiles,which were divided into a training set and a validation set in a ratio of 7∶3,and the data were downscaled using two different downscaling methods,namely principal component analysis(PC A)and kernal principal component analysis(kernal-PCA),respectively,and the cosine similarity(cosine)kernel was se-lected as the best kernel function for kernal-PCA.Finally the PCA and kernal-PCA dimensionality reduction processed data are trained by k-nearest neighbour algorithm(KNN)respectively.The re-sults show that the model accuracy of kernal-PCA+KNN(95.17％)is better than that of PCA+KNN model(92.34％).The study shows that the kernal-PCA+KNN algorithm can achieve the improve-ment of the recognition accuracy of 15 types of waste textiles,and provide a strong technical support for the online near infrared automatic sorting of waste textiles.关键词
废旧纺织物/主成分分析(PCA)/核主成分分析(kernel-PCA)/k-近邻算法(KNN)/分类识别Key words
waste textiles/principal component analysis(PCA)/kernel principal component anal-ysis(kernel-PCA)/k-nearest neighbour(KNN)algorithm/classification recognition分类
化学化工引用本文复制引用
李宁宁,刘正东,王海滨,韩熹,李文霞..基于PCA+KNN和kernal-PCA+KNN算法的废旧纺织物鉴别[J].分析测试学报,2024,43(7):1039-1045,7.基金项目
中国纺织工业联合会"纺织之光"应用基础研究项目(J202204) （J202204）
研究生教改"新工科"背景下纺织科学与工程创新实践中心建设(NHFZ20230202) （NHFZ20230202）
