Manifold Regularized Principal Component Analysis Method Using L2,p-Norm

Minghua Wan, Xichen Wang, Hai Tan () and Guowei Yang
Additional contact information
Minghua Wan: School of Information Engineering, Nanjing Audit University, Nanjing 211815, China
Xichen Wang: School of Information Engineering, Nanjing Audit University, Nanjing 211815, China
Hai Tan: School of Information Engineering, Nanjing Audit University, Nanjing 211815, China
Guowei Yang: School of Information Engineering, Nanjing Audit University, Nanjing 211815, China

Mathematics, 2022, vol. 10, issue 23, 1-17

Abstract: The main idea of principal component analysis (PCA) is to transform the problem of high-dimensional space into low-dimensional space, and obtain the output sample set after a series of operations on the samples. However, the accuracy of the traditional principal component analysis method in dimension reduction is not very high, and it is very sensitive to outliers. In order to improve the robustness of image recognition to noise and the importance of geometric information in a given data space, this paper proposes a new unsupervised feature extraction model based on l 2 , p -norm PCA and manifold learning method. To improve robustness, the model method adopts l 2 , p -norm to reconstruct the distance measure between the error and the original input data. When the image is occluded, the projection direction will not significantly deviate from the expected solution of the model, which can minimize the reconstruction error of the data and improve the recognition accuracy. To verify whether the algorithm proposed by the method is robust, the data sets used in this experiment include ORL database, Yale database, FERET database, and PolyU palmprint database. In the experiments of these four databases, the recognition rate of the proposed method is higher than that of other methods when p = 0.5 . Finally, the experimental results show that the method proposed in this paper is robust and effective.

Keywords: principal component analysis; manifold learning; features extracting; l 2, p -norm; neighborhood preserving embedding (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/10/23/4603/pdf (application/pdf)
https://www.mdpi.com/2227-7390/10/23/4603/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:10:y:2022:i:23:p:4603-:d:993887

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().