MATLAB: How can i use pca as a filter

principle component analysisStatistics and Machine Learning Toolbox

I am using PCA as filter. But as data should be obtained with maximum principle component having 90% contribution. But in my code i am not getting contribution above 90%. As i am increasing my no of observation contribution is decresing. I have used matlab function: pca(x). Please guide me what should i do to retain contribution level above 90%.

Best Answer

Use more principal components. If you're just using the first (strongest) principal component, then yeah, it's quite possible it doesn't explain more than 90% of the variation/pattern/shape of the input observations. If you use all of them then it will explain 100%. So use as many of them as you need to reach 90%.

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MATLAB: How to find all the eigenvectors of a very large, sparse, symmetric, positive-definite matrix in MATLAB 7.8 (R2009a)

You are receiving the out of memory error because EIGS tries to return all of the eigenvectors at once and it is not possible to store all the 22500 eigenvectors on a 32-bit machine.

As a workaround, knowledge of the eigenvalues can be used to access the eigenvectors in chunks using the function EIGS as follows:

[V D] = eigs(A,100,0);

The above code will give 100 eigenvectors corresponding to eigenvalues closest to 0. For better results, never use an eigenvalue as the shift (third argument in EIGS).

When retreiving the eirgenvectors, note that although the matrix is sparse the eigenvectors are most likely dense.

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I think that the code expects you provide x' or x transpose where the rows are 500 and columns are 5

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MATLAB: How to find all the eigenvectors of a very large, sparse, symmetric, positive-definite matrix in MATLAB 7.8 (R2009a)

MATLAB: Dimensional reduction by PCA

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