Precompute Gram matrix only for support vectors in sklearn SVM
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I have trained an SVM classifier using a precomputed kernel matrix of shape (n_train, n_train). Also, I'm able to successfully predict using a precomputed kernel matrix of shape (n_test, n_train).
Now I'm wondering, since only some of the train vectors are support vectors, is it possible to classify the test samples using a kernel matrix of shape (n_test, n_support_vectors) somehow?
I have considered using a sparse matrix. However, that would force the training kernel matrix to be sparse, too, and that is not supported.
python scikit-learn svm
asked Nov 11 at 19:58
Ikecuc
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I have trained an SVM classifier using a precomputed kernel matrix of shape (n_train, n_train). Also, I'm able to successfully predict using a precomputed kernel matrix of shape (n_test, n_train).
Now I'm wondering, since only some of the train vectors are support vectors, is it possible to classify the test samples using a kernel matrix of shape (n_test, n_support_vectors) somehow?
I have considered using a sparse matrix. However, that would force the training kernel matrix to be sparse, too, and that is not supported.
python scikit-learn svm
asked Nov 11 at 19:58
Ikecuc
11
add a comment |
up vote
0
down vote
favorite
up vote
0
down vote
favorite
I have trained an SVM classifier using a precomputed kernel matrix of shape (n_train, n_train). Also, I'm able to successfully predict using a precomputed kernel matrix of shape (n_test, n_train).
Now I'm wondering, since only some of the train vectors are support vectors, is it possible to classify the test samples using a kernel matrix of shape (n_test, n_support_vectors) somehow?
I have considered using a sparse matrix. However, that would force the training kernel matrix to be sparse, too, and that is not supported.
python scikit-learn svm
asked Nov 11 at 19:58
Ikecuc
11
I have trained an SVM classifier using a precomputed kernel matrix of shape (n_train, n_train). Also, I'm able to successfully predict using a precomputed kernel matrix of shape (n_test, n_train).
Now I'm wondering, since only some of the train vectors are support vectors, is it possible to classify the test samples using a kernel matrix of shape (n_test, n_support_vectors) somehow?
I have considered using a sparse matrix. However, that would force the training kernel matrix to be sparse, too, and that is not supported.
python scikit-learn svm
python scikit-learn svm
asked Nov 11 at 19:58
Ikecuc
11
asked Nov 11 at 19:58
Ikecuc
11
asked Nov 11 at 19:58
Ikecuc
11
asked Nov 11 at 19:58
Ikecuc
11
asked Nov 11 at 19:58
Ikecuc
11
11
add a comment |
add a comment |
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