清空筛选条件
清空筛选条件

Principal component Analysis example on Matlab

3 次查看（过去 30 天）
KaMu
KaMu 2014-6-25
I think there is something wrong here. I am applying the PCA through the statistical tool. I have a data XData that range from 1-0.9 with 512 dimension. I am using the PCA to reduce the dimension. I was following the example on: http://www.mathworks.com/help/stats/feature-transformation.html#f75476
I have applied : [coeff,score,latent] = pca(XData);
Then to transform the coefficients so they are orthonormal :
coefforth = inv(diag(std(XData)))*wcoeff;
when I test the data using : cscores = zscore(XData)*coefforth;
I can see that cscores and score are both different. Note that I didn’t need to wight my data.
I have also tried with a new data set :

请先登录，再回答此问题。

回答（0 个）

类别

在 Help Center 和 File Exchange 中查找有关 Dimensionality Reduction and Feature Extraction 的更多信息

标签

产品

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!

Translated by