I am trying to get the number of components needed to be used for classification. I have read a similar question Finding the dimension with highest variance using scikit-learn PCA and the scikit documents about this:
However, this still did not solve my question. All of my PCA components are super big and of cause I could select all of them but if I do so PCA will be useless.
I also read the PCA library in scikit learn http://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html It indicates thatL:
if n_components == ‘mle’, Minka’s MLE is used to guess the dimension if 0 < n_components < 1, select the number of components such that the amount of variance that needs to be explained is greater than the percentage specified by n_components
However I cannot find any more information about use this techniques for analysis n_components of PCA
Here is my code of PCA analysis:
from sklearn.decomposition import PCA
pca = PCA()
pca.fit(x_array_train)
print(pca.explained_variance_)
result:
[ 6.58902714e+50 6.23266555e+49 2.93568652e+49 2.25418736e+49
1.10063872e+49 3.25107359e+40 4.72113817e+39 1.40411862e+39
4.03270198e+38 1.60662882e+38 3.20028861e+28 2.35570241e+27
1.54944915e+27 8.05181151e+24 1.42231553e+24 5.05155955e+23
2.90909468e+23 2.60339206e+23 1.95672973e+23 1.22987336e+23
9.67133111e+22 7.07208772e+22 4.49067983e+22 3.57882593e+22
3.03546737e+22 2.38077950e+22 2.18424235e+22 1.79048845e+22
1.50871735e+22 1.35571453e+22 1.26605081e+22 1.04851395e+22
8.88191944e+21 6.91581346e+21 5.43786989e+21 5.05544020e+21
4.33110823e+21 3.18309135e+21 3.06169368e+21 2.66513522e+21
2.57173046e+21 2.36482212e+21 2.32203521e+21 2.06033130e+21
1.89039408e+21 1.51882514e+21 1.29284842e+21 1.26103770e+21
1.22012185e+21 1.07857244e+21 8.55143095e+20 4.82321416e+20
2.98301261e+20 2.31336276e+20 1.31712446e+20 1.05253795e+20
9.84992112e+19 8.27574150e+19 4.66007620e+19 4.09687463e+19
2.89855823e+19 2.79035170e+19 1.57015298e+19 1.39218538e+19
1.00594159e+19 7.31960049e+18 5.29043685e+18 5.29043685e+18
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5.29043685e+18 5.24952686e+18 2.09685699e+18 4.16588190e+17]
I tried PCA(n_components = 'mle') however I got these errors ..
Traceback (most recent call last):
File "xx", line 166, in <module>
pca.fit(x_array_train)
File "xx", line 225, in fit
self._fit(X)
File "/Users/lib/python2.7/site-packages/sklearn/decomposition/pca.py", line 294, in _fit
n_samples, n_features)
File "/Users/lib/python2.7/site-packages/sklearn/decomposition/pca.py", line 98, in _infer_dimension_
ll[rank] = _assess_dimension_(spectrum, rank, n_samples, n_features)
File "/Users/lib/python2.7/site-packages/sklearn/decomposition/pca.py", line 83, in _assess_dimension_
(1. / spectrum_[j] - 1. / spectrum_[i])) + log(n_samples)
ValueError: math domain error
Really appreciate for any helps...
I am not using Python, but I did something you need in C++ & opencv. Hope you succeed in converting it to whatever language.
// choose how many eigenvectors you want:
int nEigensOfInterest = 0;
float sum = 0.0;
for (int i = 0; i < mEiVal.rows; ++i)
{
sum += mEiVal.at<float>(i, 0);
if (((sum * 100) / (sumOfEigens)) > 80)
{
nEigensOfInterest = i;
break;
}
}
logfile << "No of Eigens of interest: " << nEigensOfInterest << std::endl << std::endl;
The basic idea is to decide "whatever %" components of you need to go ahead with. I chose those to be 80. mEiVal is column matrix of eigen values sorted in descending order. sumOfEigens is sum of all the eigen values.
I have no experience with scikit-learn, please let me know, I'll delete the answer.