PCA ideas as to decompose the covariance matrix of the image, the direction vector obtained after decomposition. The data is then separately projected up to a certain direction, to obtain an image similar to the original image. Of course, with a maximum value corresponding to the characteristic feature vector direction to get the best image. Therefore, PCA method can be used as a method of dimensionality reduction. Leave a better image in some directions, and discard those in the other direction a bad image.

PCA_K.m

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