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Is it planned to offer the kernel based version of PCA ?
Pardon my ignorance but how difficult is it to extend to PCA implementation, i would like to help ?
Thanks for the awesome CUDA library!
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#### Describe the bug
Hi,
I am trying to carry a Kernel PCA on a very large dataset (80 000 points). Using the `sklearn.decomposition.KernelPCA` function, this leads to a memory error, as the …
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The only text accompanying this [kernel PCA example](http://scikit-learn.org/stable/auto_examples/decomposition/plot_kernel_pca.html) is:
> This example shows that Kernel PCA is able to find a proj…
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Would be great if you could implement a kernel pca running on cuda
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Where the idea for code for kernel PCA `reconstruct` in `MultivariateStats.jl`https://github.com/JuliaStats/MultivariateStats.jl/blob/30aea0c2832f5a0a0574641433774eecc57aba92/src/kpca.jl#L61 is taken …
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Kernel PCA seems to do strange things, in this easy task you could
Create meaningful data for kPCA for python example
compare against other implementation with same data/parameters in unit-test,
upda…
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A ideia é estudar o efeito do componente único das matrizes de Variância e Covariância na formação de Portfólio usando Kernel PCA.
Sob essa ótica há outros componentes ? O que eles representariam ? M…
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Hello,
I'm using kernel pca to reduce dimensionality and I need eigenvalues and eigenvectors. In PCA, I know pca.explained_variance_ is eigenvalues and pca.components_ is eigenvectors. I read the s…
s0rel updated
2 years ago
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### Is there an existing issue for this?
- [X] I have searched the existing issues
### Feature Description
Algorithms Used
KNN Classifier
Logestic Regression
Decision Tree Classifier
Linear SVC…
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Before we refactored the testing infrastructure, part of my debugging process was to run the tests until they failed at the first test, inspect the stacktrace, and then fix that issue, slowly moving t…