Paper Title:
Why Can SVM Be Performed in PCA Transformed Space for Classification?
  Abstract

PCA plus SVM is a popular framework for classification problems. On the one hand, it can avoid the SVM kernel Gram to become overlarge and save storage space. On the other hand, PCA plus SVM approach has been verified effective for classification by experience. This paper focuses on building a theoretical foundation for this framework. By derivation, the equivalence relation between PCA plus SVM and LDA is discovered for binary classification. Moreover, we give a specific analysis about the framework to validate our viewpoint compared with SVM and combined SVM/LDA on ORL face database, Yale face database and real world benchmark data. The experimental results indicate that SVM can be performed in PCA transformed space for classification.

  Info
Periodical
Advanced Materials Research (Volumes 181-182)
Edited by
Qi Luo and Yuanzhi Wang
Pages
1031-1037
DOI
10.4028/www.scientific.net/AMR.181-182.1031
Citation
J. Zhang, L. L. Bo, J. W. Xu, S. H. Park, "Why Can SVM Be Performed in PCA Transformed Space for Classification?", Advanced Materials Research, Vols. 181-182, pp. 1031-1037, 2011
Online since
January 2011
Export
Price
$32.00
Share

In order to see related information, you need to Login.

In order to see related information, you need to Login.

Authors: Xiao Ping Liu, Gui Yun Xu
Chapter 6: Materials and Mechanics Information System
Abstract:Hybrid discriminant analysis (HDA) can overcome small sample problems and outperform PCA and LDA by unifying principal component analysis...
671
Authors: Shi Ping Li, Yu Cheng, Hui Bin Liu, Lin Mu
Chapter 1: Mechanic Manufacturing System and Automation
Abstract:Linear Discriminant Analysis (LDA) [1] is a well-known method for face recognition in feature extraction and dimension reduction....
58
Authors: Ke Guo, Yi Zhu, Ye San
Chapter 1: Mechatronics
Abstract:Fault diagnosis of analog circuits is essential for guaranteeing the reliability and maintainability of electronic systems. Analog circuit...
1130
Authors: Xiao Hong Wu, Xing Xing Wan, Bin Wu, Fan Wu
Chapter 5: Control, Measurement and Monitoring: Technologies and Solutions
Abstract:Classification of apple is an important link in postharvest commercialization processing. To realize the non-destructive, rapid and effective...
524
Authors: Xiao Hong Wu, Wen Jie Xu, Bin Wu, Sheng Wei Qiu
Chapter 5: Control, Measurement and Monitoring: Technologies and Solutions
Abstract:Principal component analysis (PCA) and kernel Fisher discriminant analysis (KFDA) were applied to grade Fuji apples combined with near...
529