Kong H., Li, X. and Wang, L. (2005) Generalized 2D Principal Component Analysis. 2005 IEEE International Joint Conference on Neural Networks, Montreal, 31 July-4 August 2005, 108-112.
https://doi.org/10.1109/IJCNN.2005.1555814

has been cited by the following article:

• TITLE: Research on Face Recognition Algorithm Based on Robust 2DPCA

  AUTHORS: Haijun Kuang, Wanzhou Ye, Ze Zhu

  KEYWORDS: 2DPCA, Face Recognition, Dimension Reduction, F Norm

  JOURNAL NAME: Advances in Pure Mathematics, Vol.11 No.2, February 22, 2021

  ABSTRACT: As a new dimension reduction method, the two-dimensional principal component (2DPCA) can be well applied in face recognition, but it is susceptible to outliers. Therefore, this paper proposes a new 2DPCA algorithm based on angel-2DPCA. To reduce the reconstruction error and maximize the variance simultaneously, we choose F norm as the measure and propose the Fp-2DPCA algorithm. Considering that the image has two dimensions, we offer the Fp-2DPCA algorithm based on bilateral. Experiments show that, compared with other algorithms, the Fp-2DPCA algorithm has a better dimensionality reduction effect and better robustness to outliers.