2 votes

Theoretical differences between KPCA and t-SNE?

Kernel PCA Kernel PCA is one of the variations of principal component analysis in which we use kernel methods to perform principal component analysis with nonlinearly separable datasets. The Kernel ...
  • 2,981
1 vote

Is it meaningfull using boxplots for representing the distribution of just four data points?

Its meaningful, but that's not important right now. You should ask yourself is it useful? A visualisation should be the answer to a question - what's the question? Does a boxplot of four points answer ...
  • 1,972
1 vote

Im looking for good neurons silmilarity metric

The animation is pretty! I think with a fully-connected network and a single hidden layer you would not expect to see any strong patterns of neuron responsibility emerge. But if you used two or three ...

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