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pca.rar
- 使用PCA进行数据预处理和数据压缩，用C++程序实现，所得主成分保存在文件中,PCA carried out using data pre-processing and data compression, and C++ Program, derived from the principal component in the document preservation
zhufneliang
- 目标的雷达散射截面(RCS)包含了丰富的目标类别信息,如何有效利用目标RCS特征对空间目标的雷 达识别具有重要意义. 文中提取中心矩作为特征向量,采用主分量分析( PCA)进一步进行特征压缩,利用支撑矢 量-Target radar cross section (RCS) contains a wealth of objective categories of information, how the effective use of target RCS characteristi
principal_components
- 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
fanglan_v60
- 是学习PCA特征提取的很好的学习资料，本程序的性能已经超过其他算法，线性调频脉冲压缩的Matlab程序。- Is a good learning materials to learn PCA feature extraction, This program has exceeded the performance of other algorithms, LFM pulse compression of the Matlab program.
faisui
- 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
hiwas
- Gabor wavelet transform and PCA face recognition code, Including compression ratio, image restoration computing uptime and peak signal to noise ratio, Stepwise linear regression.
gpldecha-e-pca-d542a9b
- PCA是一种非线性降维方法特别适合于概率分布，得到了指数族PCA的POMDPs压缩。(Matlab implementation of E-PCA which is a non-linear dimensionality reduction method particularly suited for probability distributions, see the paper Exponential Family PCA for Belief Compression in POMDPs.)
pca using svd
- 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
ncPCA
- 复数数据处理的非环PCA压缩，结合了PCA和复数数据的环形特性(Non loop PCA compression of complex data processing, combining the ring characteristics of PCA and plural data)