US 11,868,432 B1
Method for extracting kansei adjective of product based on principal component analysis and explanation (PCA-E)
Wu Zhao, Chengdu (CN); Xin Guo, Chengdu (CN); Miao Yu, Chengdu (CN); Kai Zhang, Chengdu (CN); Wei Jiang, Chengdu (CN); Chong Jiang, Chengdu (CN); Bing Lai, Chengdu (CN); Yiwei Jiang, Chengdu (CN); Jun Li, Chengdu (CN); Bo Wu, Chengdu (CN); and Xingyu Chen, Chengdu (CN)
Assigned to SICHUAN UNIVERSITY, Chengdu (CN)
Filed by SICHUAN UNIVERSITY, Chengdu (CN)
Filed on Jun. 2, 2023, as Appl. No. 18/204,973.
Application 18/204,973 is a continuation of application No. PCT/CN2022/125338, filed on Oct. 14, 2022.
Claims priority of application No. 202210684413.0 (CN), filed on Jun. 16, 2022.
Int. Cl. G06F 40/00 (2020.01); G06F 18/2135 (2023.01); G06F 17/16 (2006.01)
CPC G06F 18/2135 (2023.01) [G06F 17/16 (2013.01)] 17 Claims
OG exemplary drawing
 
1. A method for extracting a kansei adjective of a product based on principal component analysis and explanation (PCA-E), comprising:
S1: constructing a product kansei evaluation vector matrix;
S2: subjecting the product kansei evaluation vector matrix to dimensionality reduction based on principal components:
S21: zero-centering each row in the product kansei evaluation vector matrix;
S22: obtaining a covariance matrix of the product kansei evaluation vector matrix after the zero-centering;
S23: subjecting the covariance matrix obtained in step S22 to orthogonal decomposition to obtain eigenvalues and eigenvectors of the covariance matrix; and
S24: arranging the eigenvalues in a descending order, and extracting the eigenvectors of first f principal components with a cumulative contribution rate greater than a first threshold to form an eigenvector matrix;
S3: obtaining a principal component load factor matrix, and constructing a principal component load factor table; and
S4: extracting kansei adjectives representing the principal components according to a kansei adjective extraction strategy.