Randomized nonlinear two-dimensional principal component analysis network for object recognition

Sun, Zhi‐Wei; Shao, Zhuhong; Shang, Yuanyuan; Li, Bicao; Wu, Jiasong; Bu, Hui

doi:10.1007/s00138-023-01371-9

Cited by 3 publications

(1 citation statement)

References 29 publications

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“…To enhance the robustness of the algorithm against outliers, Li et al [32] put forward an L1-2D 2 PCANet and replaced the PCA algorithm with L1-2DPCA. In order to capture nonlinear structures within data and more representational image features, Sun et al [33] proposed a nonlinear two-dimensional PCANet (RN2DPCANet). An approximate method based on a Gaussian kernel was used to map the original image to random feature space.…”

Section: Introductionmentioning

confidence: 99%

A Bi-Directional Two-Dimensional Deep Subspace Learning Network with Sparse Representation for Object Recognition

Li,

Feng,

Wang

et al. 2023

Electronics

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A principal component analysis network (PCANet), as one of the representative deep subspace learning networks, utilizes principal component analysis (PCA) to learn filters that represent the dominant structural features of objects. However, the filters used in PCANet are linear combinations of all the original variables and contain complex and redundant principal components, which hinders the interpretability of the results. To address this problem, we introduce sparse constraints into a subspace learning network and propose three sparse bi-directional two-dimensional PCANet algorithms, including sparse row 2D2PCANet (SR2D2PCANet), sparse column 2D2PCANet (SC2D2PCANet), and sparse row–column 2D2PCANet (SRC2D2PCANet). These algorithms perform sparse operations on the projection matrices in the row, column, and row–column direction, respectively. Sparsity is achieved by utilizing the elastic net to shrink the loads of the non-primary elements in the principal components to zero and to reduce the redundancy in the projection matrices, thus improving the learning efficiency of the networks. Finally, a variety of experimental results on ORL, COIL-100, NEC, and AR datasets demonstrate that the proposed algorithms learn filters with more discriminative information and outperform other subspace learning networks and traditional deep learning networks in terms of classification and run-time performance, especially for less sample learning.

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Section: Introductionmentioning

confidence: 99%