2
votes
Accepted
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
Accepted
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 ...
- 807
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