主成分分析法在商品分类指标体系构建中的应用
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摘要
在对商品分类时,为了对分类进行全面分析,建立完善的分类时标体系,往往提出很多与其相关的指标。一般情况下,这些指标间会存在一定的相关关系,即某些指标对商品分类问题的反映信息是有重叠的。论文应用主成分分析方法对复杂的商品分类指标体系进行降维,在实现减少分类指标的同时,尽量减少原指标体系所包含信息的损失。
In order to comprehensively analyze the classification and establish a perfect classification time scale system,many related indicators are often put forward in the classification of commodities. Generally,there is a certain correlation between these indicators,that is,some indicators reflect overlapping information on the classification of commodities. In this paper,the principal component analysis method is applied to reduce the dimension of the complex commodity classification index system. While reducing the classification index,the loss of information contained in the original index system is minimized.
In order to comprehensively analyze the classification and establish a perfect classification time scale system,many related indicators are often put forward in the classification of commodities. Generally,there is a certain correlation between these indicators,that is,some indicators reflect overlapping information on the classification of commodities. In this paper,the principal component analysis method is applied to reduce the dimension of the complex commodity classification index system. While reducing the classification index,the loss of information contained in the original index system is minimized.
引文
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[12]朱蕾.基于改进主成分分析的低压配电网供电所综合评价方法[J].电力工程技术,2018(4):187-192.ZHU Lei.Comprehensive Evaluation Method Basedon Improved Principal Component Analysis of Low Voltage Distribution Network Power Substations[J].Electric Power Engineering Technology,2018(4):187-192.
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[14]唐志晶.基于主成分分析的能源结构研究[J].无线互联科技,2018(14):107-108.TANG Zhijing.Research on energy structure based on principal component analysis[J].Wireless Internet Technology,2018(14):107-108.
[15]王双燕.基于主成分分析的河南省能源安全评价研究[J].中原工学院学报,2011(06):44-46.WANG Shuangyan.Study on the Evaluation of Henan's Energy Security Based on PCA[J].Journal of Zhongyuan University of Technology,2011(06):44-46.
[16]王丽.基于主成分分析和统计建模的数据预测[J].工业控制计算机,2018(7):85-87.WANG Li.Data Prediction Based on Principal Component Analysis and Statistical Modeling[J].Industrial Control Computer,2018(7):85-87.
[17]吕岩威,李平.一种加权主成分距离的聚类分析方法[J].统计研究,2016,(11):102-108.LV Yanwei,LI Ping.A Clustering Analysis Method of Weighted Principal Component Distance[J].Statistical Research,2016,(11):102-108.
[2]汪办兴.我国银行贷款违约损失率影响因素的实证分析,2007(14):121.WANG Banxing.Empirical Research on Impact Factors of LGD of China‘s Commercial Banks’Loan[J].Journal of Shanghai University of Finance and Economics,2007(14):121.
[3]苏越.偏最小二乘法中主成分数确定的新方法[J].计算机与应用化学,2001,18(3):237-239.SU Yue,GUO Yinlong.The Novel Algorithm for Estimation of the Number of Principal Component in Partial Least-Squares Method[J].Computers and Applied Chemistry,2001,18(3):237-239.
[4]李桂华.影响高校大学生体育活动开展的主成因子分析[J].南京体育学院学报,2006,20(6):96-98.LI Guihua,LIU Junmei.Analysis on the Main Factor Effect the College Students'Exercise[J].Journal of Nanjing Sport Institute,2006,20(6):96-98.
[5]黄云腾.多因变量多元线性模型主成分型预测的最优性判别[J].桂林电子科技大学学报,2013,33(5):412-415.HUANG Yunteng.Superiority discrimination of principal component prediction in the multivariate linear model with multiple dependent variables[J].Journal of Guilin University of Electronic Technology,2013:33(5):412-415.
[6]马滕飞.基于主成分分析-神经网络的医学图像刚性配准方法[J].科学技术与工程,2011(22):101-103.MA Tengfei.Rigid Medical Image Registration Using PCANeural Network[J].Science Technology and Engineering,2011(22):101-103.
[7]潘玉丽,张秉森,许倩.主成分分析在织物染色计算机配色中的应用研究[J].青岛大学学报(工程技术版),2011(04):89-92.PAN Yuli,ZHANG Bingsen,XU Qian.Research on Application of Principal Component Analysis in Computer Color Matching for Textile Dyeing[J].Journal of Qingdao University(Engineering&Technology Edition),2011(04):89-92.
[8]郑和忠.基于主成分分析和核主成分分析的地震属性优化的研究[J].青岛大学学报(自然科学版),2017(3):36-37.ZHENG Hezhong.Seismic Attribute Optimization Research Based on Principal Component Analysis and Kernel Principal Component Analysis[J].Journal of Qingdao University(Natural Science Edition),2017(3):36-37.
[9]余肖生,司新霞.基于聚类分析的元搜索引擎模型[J].重庆理工大学学报(自然科学),2011(06):12-15.YU Xiaosheng,SI Xinxia.Research on Meta-Search Engine Model based on Cluster Analysis[J].Journal of Chongqing Institute of Technology,2011(06):12-15.
[10]韩梅丽,李霖.基于ARIMA的不稳定需求备件测模型研究[J].物流工程与管理,2016(2):48-50.HAN Meili,LI Lin.Spare Parts Inventory Forecasting Model Research Based On ARIMA Model[J].Logistics Engineering and Management,2016(2):48-50.
[11]韩玉.加权主成分距离聚类分析方法的有效性[J].东北电力大学学报,2018,38(4):94-98.HAN Yu.The Effectiveness of the Weighted Principal Component Distance Clustering Analysis Method[J].Journal of Northeast Electric Power University,2018,38(4):94-98.
[12]朱蕾.基于改进主成分分析的低压配电网供电所综合评价方法[J].电力工程技术,2018(4):187-192.ZHU Lei.Comprehensive Evaluation Method Basedon Improved Principal Component Analysis of Low Voltage Distribution Network Power Substations[J].Electric Power Engineering Technology,2018(4):187-192.
[13]彭宇文.基于全局主成分分析的湖南省各地级市经济增长质量评价[J].湖南工业大学学报(社会科学版),2018,23(4):14-18.PENG Yuwen.Quality Evaluation of Economic Growth of Local Cities in Hunan Province Based on Global Principal Component Analysis[J].Journal of Hunan University of Technology(Social Science Edition),2018,23(4):14-18.
[14]唐志晶.基于主成分分析的能源结构研究[J].无线互联科技,2018(14):107-108.TANG Zhijing.Research on energy structure based on principal component analysis[J].Wireless Internet Technology,2018(14):107-108.
[15]王双燕.基于主成分分析的河南省能源安全评价研究[J].中原工学院学报,2011(06):44-46.WANG Shuangyan.Study on the Evaluation of Henan's Energy Security Based on PCA[J].Journal of Zhongyuan University of Technology,2011(06):44-46.
[16]王丽.基于主成分分析和统计建模的数据预测[J].工业控制计算机,2018(7):85-87.WANG Li.Data Prediction Based on Principal Component Analysis and Statistical Modeling[J].Industrial Control Computer,2018(7):85-87.
[17]吕岩威,李平.一种加权主成分距离的聚类分析方法[J].统计研究,2016,(11):102-108.LV Yanwei,LI Ping.A Clustering Analysis Method of Weighted Principal Component Distance[J].Statistical Research,2016,(11):102-108.