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How to reduce the size of the trained SVM model in Opencv3.0?

I have three image classify problem, and the size of feature dimension of LBP are 6400, with number of samples 6000. Now I have trained the three SVM models. Each size of the model is bout 20M. Because I want to transplant the project to android, so I want to compress the total size of three model within 20M.

The image's lbp feature is sparse, So I try to use PCA to perform feature reduction. But the size mapping matrix of 500 dimension each classify problem is about 30M.

Is there are any other way to solve my problem?

How to reduce the size of the trained SVM model in Opencv3.0?

I have three image classify problem, and ~~the size of~~ feature dimension of LBP each problem are 6400, with number of samples 6000. Now I have trained the three SVM models. Each size of the model is bout 20M. Because I want to transplant the project to android, so I want to compress the total size of three ~~model~~ models within 20M.

The image's lbp feature is sparse, So I ~~try~~ tried to use PCA to perform feature reduction. But the size mapping matrix of 500 dimension each classify problem is about ~~30M.~~30M, and that is too big.

Is there are any other way to solve my problem?

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updated 2017-07-09 22:00:39 -0600

berak
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How to reduce the size of the trained SVM model in Opencv3.0?

I have three image classify problem, and feature dimension of LBP each problem are 6400, with number of samples 6000. Now I have trained the three SVM models. Each size of the model is bout 20M. Because I want to transplant the project to android, so I want to compress the total size of three models within 20M.

The image's lbp feature is sparse, So I tried to use PCA to perform feature reduction. But the size mapping matrix of 500 dimension each classify problem is about 30M, and that is too big.

Is there are any other way to solve my problem?