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Dimensionality reduction using ICA
Guru
Rapidminer has got an operator 'ind. component analysis' for dimensionality reduction. We have to choose 'fixed no' in parameter configuration of the operator and accordingly the number of attributes are chosen.
unlike PCA - where the reduction of attribute is done based on decreasing order of eigen values /variance ,
in ICA - it is not clear on what basis the features are chosen. In my case: i have six attributes means , i get ic 1, ic2 to ic6.
if I choose 4 attribute it end up in ic1 , ic2 , ic3 , ic4. But it is not clear how these independant components are chosen for reduced dimension requirement. Please let me know. thanks.
regds
thiru
unlike PCA - where the reduction of attribute is done based on decreasing order of eigen values /variance ,
in ICA - it is not clear on what basis the features are chosen. In my case: i have six attributes means , i get ic 1, ic2 to ic6.
Number of Components: 6 Resulting attribute weights (from first component): attr1: 0.019 attr2: -0.040 attr3: -0.055 attr4: 0.135 attr5: -0.115 attr6: -0.955
if I choose 4 attribute it end up in ic1 , ic2 , ic3 , ic4. But it is not clear how these independant components are chosen for reduced dimension requirement. Please let me know. thanks.
regds
thiru
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