PCA的思想为将图像的协方差矩阵分解，获得分解后的方向向量。然后将数据分别投影到某一个方向上去，获得与原图象近似的图像。当然,与最大特征值所对应的特征向量方向获得最好的图像。因此，PCA方法可以作为降维的一种方法。留下在某些方向较好的图像，而抛弃那些在另外一些方向上不好的图像。-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.