Machine Learing by Andrew Ng --- PCA

U can use PCA to speed up your learning algorithm,but before it ,U’d better to run your learning algorithm with no PCA first(This is What Andrew Ng said).And U can also compress data by using PCA.amazing!
PCA is actually an algorithm to reduce your features,also say Dimensionality Reduction.

steps:

• Feature Normalization.
• Computing the covariance matrix of the data.
• Using the build-in function ’ svd ’ to obtain U and S(i.e. [U,S,V]=svd(Sigma),Sigma is the covariance matrix we hava computed previously)
• Projecting data.(with U and S)
• U is a n by n matrix (n is the original number of the data’features)
• More important things already mentioned in the lecture notes.

My solution: