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Results tagged with pca
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user 41749
Principal component analysis (PCA) is a linear dimensionality reduction technique. It reduces a multivariate dataset to a smaller set of constructed variables preserving as much information (as much variance) as possible. These variables, called principal components, are linear combinations of the input variables.
92
votes
4
answers
51k
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What're the differences between PCA and autoencoder?
Both PCA and autoencoder can do demension reduction, so what are the difference between them? In what situation I should use one over another? …
- 13.1k
86
votes
3
answers
77k
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What is the difference between ZCA whitening and PCA whitening?
I am confused about ZCA whitening and normal whitening (which is obtained by dividing principal components by the square roots of PCA eigenvalues). … As far as I know,
$$\mathbf x_\mathrm{ZCAwhite} = \mathbf U \mathbf x_\mathrm{PCAwhite},$$ where $\mathbf U$ are PCA eigenvectors.
What are the uses of ZCA whitening? …
- 13.1k