⑴ matlab中pca
1，4 matlab是有幫助文檔的，我沒有明白你所指的去中心化處理是什麼，PCA的結果在數組自己的維度。
以下是幫助文檔，請仔細閱讀
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，他們分解矩陣的方式是不同的。我建議你翻看wikipedia裡面SVD和PCA的說明，裡面公式很清晰了
⑵ 有沒有大神站到用Matlab的PLS工具箱怎麼做主成分分析
1、參數mA代表A的均值，也就是mean(A)。
其實這個參數完全沒必要，因為可以從參數A計算得到。
2、解釋一下你問的兩個語句的含義：
Z=(A-repmat(mA,m,1)); 作用是去除直流成分T=Z*Z'; 計算協方差矩陣的轉置
3、關於函數的調用：
MATLAB統計工具箱中有函數princomp，也是進行主成分分析的（2012b之後有函數pca），基本調用格式：
[pc, score] = princomp(x)其中，輸入參數x相當於你這個函數的A，輸出參數score相當於你這里的pcaA，而pc大致相當於你這里的V（符號相反）。具體說明請參考函數的文檔。
⑶ matlab中的降維函數是什麼
drttoolbox : Matlab Toolbox for Dimensionality Rection是Laurens van der Maaten數據降維的工具箱。
裡面囊括了幾乎所有的數據降維演算法：
- 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')