Image Classification Algorithm Combining Principal Component Analysis and Complex Valued Neural Network 


Vol. 48,  No. 8, pp. 1012-1022, Aug.  2023
10.7840/kics.2023.48.8.1012


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  Abstract

This paper proposes a PCA-CVNN-ECBP (PCA-CVNN with Error-Correcting Backpropagation) algorithm that combines dimensionality reduction using Principal Component Analysis (PCA) and a Complex Valued Neural Network (CVNN) for image processing and classification. The algorithm is validated through experiments, which show improved performance in image processing and classification. The paper also discusses the importance of dimensionality reduction in image processing and classification, the PCA method for dimensionality reduction, and error correction and backpropagation methods in CVNNs. The results of the experiments prove that the proposed algorithm is an effective method.

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[IEEE Style]

S. Ko and M. Park, "Image Classification Algorithm Combining Principal Component Analysis and Complex Valued Neural Network," The Journal of Korean Institute of Communications and Information Sciences, vol. 48, no. 8, pp. 1012-1022, 2023. DOI: 10.7840/kics.2023.48.8.1012.

[ACM Style]

Sung-kyun Ko and Min-ho Park. 2023. Image Classification Algorithm Combining Principal Component Analysis and Complex Valued Neural Network. The Journal of Korean Institute of Communications and Information Sciences, 48, 8, (2023), 1012-1022. DOI: 10.7840/kics.2023.48.8.1012.

[KICS Style]

Sung-kyun Ko and Min-ho Park, "Image Classification Algorithm Combining Principal Component Analysis and Complex Valued Neural Network," The Journal of Korean Institute of Communications and Information Sciences, vol. 48, no. 8, pp. 1012-1022, 8. 2023. (https://doi.org/10.7840/kics.2023.48.8.1012)
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pISSN : 1226 - 4717
eISSN : 2287 - 3880