In view of the shortcomings of large image space occupation and high dimension when representing features, the basic principle of principal component analysis (PCA) is systematically introduced. A basic model for image data compression and reconstruction using PCA is proposed. Experimental results show that using PCA can effectively reduce the dimension of data, extract features, realize image compression, and reconstruct images according to actual needs.
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