.png){kind=icon}
.png){kind=icon}
Return
KPCA method based on within- class auxiliary training samples and its application to pattern classification
.png){kind=icon}
PRE
AI
DOI：10.1007/s10044-016-0531-5.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Principal component analysis (PCA) and kernel principal component analysis (KPCA) are classical feature extraction methods. However, PCA and KPCA are unsupervised learning methods which always maximize the overall variance and ignore the information of within-class and between-class. In this paper, we propose a simple yet effective strategy to improve the performance of PCA and then this strategy is generalized to KPCA. The proposed methods utilize within-class auxiliary training samples, which are constructed through linear interpolation method. These withinclass auxiliary training samples are used to train and get the principal components. In contrast with conventional PCA and KPCA, our proposed methods have more discriminant information. Several experiments are respectively conducted on XM2VTS face database, United States Postal Service (USPS) handwritten digits database and three UCI repository of machine learning databases, experimental results illustrate the effectiveness of the proposed method.
Keywords:
Principal component analysis (PCA)
Kernel principal component analysis (KPCA)
Auxiliary training sample
Pattern classification
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
2
Papers:
1.9K
Citations:
1.9K