This tutorial is designed to give the reader an understanding of Principal Components Analysis (PCA). PCA is a useful statistical technique that has found application in fields such as face recognition and image compression, and is a common techn

结合PCA的尺度不变特征变换(SIFT)算法，包括压缩比、运行时间和计算复原图像的峰值信噪比,这是第二能量熵的matlab代码。- Combined with PCA scale invariant feature transform (SIFT) algorithm, Including compression ratio, image restoration computing uptime and peak signal to noise ratio, This is the second

Gabor wavelet transform and PCA face recognition code, Including compression ratio, image restoration computing uptime and peak signal to noise ratio, Stepwise linear regression.

Principal components analysis is one of a family of techniques for taking high-dimensional data, and using the dependencies between the variables to represent it in a more tractable, lower-dimensional form, without losing too much information. PC