计算机科学 ›› 2017, Vol. 44 ›› Issue (Z11): 202-206.doi: 10.11896/j.issn.1002-137X.2017.11A.042

• 模式识别与图像处理 • 上一篇    下一篇

PCA与2DPCA的关系

闫荣华,彭进业,汶德胜   

  1. 西北工业大学电子信息学院 西安710072;中国科学院西安光学精密机械研究所 西安710119,西北工业大学电子信息学院 西安710072;西北大学信息科学与技术学院 西安710127,中国科学院西安光学精密机械研究所 西安710119
  • 出版日期:2018-12-01 发布日期:2018-12-01
  • 基金资助:
    本文受基于图像的植物识别和检索研究(61272285),国家863课题:媒体大数据的结构化描述方法研究(2014AA015201)资助

Relationship between PCA and 2DPCA

YAN Rong-hua, PENG Jin-ye and WEN De-sheng   

  • Online:2018-12-01 Published:2018-12-01

摘要: 主成分分析和二维主成分分析是两种经典的数据变换方法。尽管许多学者对PCA和2DPCA进行了大量的研究和实验,但并未给出PCA与2DPCA之间的联系。文中给出二者之间的联系,即PCA与2DPCA在优化时具有相同的最优目标值,同时通过理论推导和在CMU-PIE与CK+库上的实验证明了这一观点。

关键词: 人脸识别,主成分分析,二维主成分分析

Abstract: Principal component analysis and two-dimensional principal component analysis are two classical data transformation techniques.Although many scholars worked on them,there’s no relationship of two methods to be given.In this paper,we gave their relationship,which is that they have the same optimal objective value in optimization.The opinion was demonstrated through theoretical derivation and extensive experiments which is tested on the CMU-PIE and CK+face database.

Key words: Face recognition,PCA,2DPCA

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