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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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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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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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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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Hey,
I'd like to use this to tune the kernel parameter for k-PCA, but all the examples are for use with labeled data. How would I go about setting up hyperopt-sklearn to allow me to tune the RBF ker…