⑴ matlab中pca
1，4 matlab是有帮助文档的，我没有明白你所指的去中心化处理是什么，PCA的结果在数组自己的维度。
以下是帮助文档，请仔细阅读
coeff = pca(X) returns the principal component coefficients, also known as loadings, for the n-by-p data matrix X. Rows of X correspond to observations and columns correspond to variables. The coefficient matrix is p-by-p. Each column of coeffcontains coefficients for one principal component, and the columns are in descending order of component variance. By default, pca centers the data and uses the singular value decomposition (SVD) algorithm.
example
coeff = pca(X,Name,Value) returns any of the output arguments in the previous syntaxes using additional options for computation and handling of special data types, specified by one or more Name,Value pair arguments.
For example, you can specify the number of principal components pca returns or an algorithm other than SVD to use.
example
[coeff,score,latent] = pca(___) also returns the principal component scores in score and the principal component variances in latent. You can use any of the input arguments in the previous syntaxes.
Principal component scores are the representations of X in the principal component space. Rows of score correspond to observations, and columns correspond to components.
The principal component variances are the eigenvalues of the covariance matrix of X.
example
[coeff,score,latent,tsquared] = pca(___) also returns the Hotelling's T-squared statistic for each observation in X.
example
[coeff,score,latent,tsquared,explained,mu] = pca(___) also returns explained, the percentage of the total variance explained by each principal component and mu, the estimated mean of each variable in X.
2. PCA 和SVD的不同是，他们分解矩阵的方式是不同的。我建议你翻看wikipedia里面SVD和PCA的说明，里面公式很清晰了
⑵ 有没有大神站到用Matlab的PLS工具箱怎么做主成分分析
1、参数mA代表A的均值，也就是mean(A)。
其实这个参数完全没必要，因为可以从参数A计算得到。
2、解释一下你问的两个语句的含义：
Z=(A-repmat(mA,m,1)); 作用是去除直流成分T=Z*Z'; 计算协方差矩阵的转置
3、关于函数的调用：
MATLAB统计工具箱中有函数princomp，也是进行主成分分析的（2012b之后有函数pca），基本调用格式：
[pc, score] = princomp(x)其中，输入参数x相当于你这个函数的A，输出参数score相当于你这里的pcaA，而pc大致相当于你这里的V（符号相反）。具体说明请参考函数的文档。
⑶ matlab中的降维函数是什么
drttoolbox : Matlab Toolbox for Dimensionality Rection是Laurens van der Maaten数据降维的工具箱。
里面囊括了几乎所有的数据降维算法：
- Principal Component Analysis ('PCA')
- Linear Discriminant Analysis ('LDA')
- Independent Component Analysis ('ICA')
- Multidimensional scaling ('MDS')
- Isomap ('Isomap')
- Landmark Isomap ('LandmarkIsomap')
- Locally Linear Embedding ('LLE')
- Locally Linear Coordination ('LLC')
- Laplacian Eigenmaps ('Laplacian')
- Hessian LLE ('HessianLLE')
- Local Tangent Space Alignment ('LTSA')
- Diffusion maps ('DiffusionMaps')
- Kernel PCA ('KernelPCA')
- Generalized Discriminant Analysis ('KernelLDA')
- Stochastic Neighbor Embedding ('SNE')
- Neighborhood Preserving Embedding ('NPE')
- Linearity Preserving Projection ('LPP')
- Stochastic Proximity Embedding ('SPE')
- Linear Local Tangent Space Alignment ('LLTSA')
- Simple PCA ('SPCA')