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Principal component analysis based on graph embedding


Dimensionality reduction plays an important role in image recognition and data mining. Traditional methods extract features from data itself and ignore the structure information of data even though it is crucial for effective representation. Considering graph embedding method can capture and model the complicated relationships among data, therefore, we consider to incorporate graph convolution learning into principal component analysis (GCPCA) to abstract more effective features in this paper. The key idea of the proposed model is embedding graph convolutional to realize linear representation by fusing the relationship of data points. Then PCA is operated on projected data to extract effective features. The model can be solved to obtain a globally optimal closed-form solution, which is convenient for implementation and practical application. Experiments on some publicly available datasets demonstrate that the proposed GCPCA model show the better performance than the existing classical algorithms in terms of classification accuracy.

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This research was supported by Beijing Municipal Science and Technology Project with no. Z191100009119013, in part by National Natural Science Foundation of China under Grant 61772048, 61672071 and 61902053, in part by the Beijing Natural Science Foundation under Grant 4172003, in part by Beijing Municipal Science and Technology Project KM201910005028 and KM202011417004.

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Correspondence to Yanfeng Sun.

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Ju, F., Sun, Y., Li, J. et al. Principal component analysis based on graph embedding. Multimed Tools Appl (2022).

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  • Dimensionality reduction
  • Graph embedding
  • Principal component analysis
  • Feature extraction