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Is PCA a linear autoencoder?
PCA is essentially a linear transformation but Auto-encoders are capable of modelling complex non linear functions. PCA is faster and computationally cheaper than autoencoders. A single layered autoencoder with a linear activation function is very similar to PCA.
Does autoencoder use backpropagation?
Autoencoder is a neural network (NN), as well as an un-supervised learning (feature learning) algorithm. It applies backpropagation, by setting the target value same as input. It tries to predict x from x, without need for labels.
What is nearly equivalent to a PCA?
A single layer auto encoder with linear transfer function is nearly equivalent to PCA, where nearly means that the WW found by AE and PCA won’t be the same–but the subspace spanned by the respective WW’s will.
What’s the difference between PCA and auto encoder?
PCA is restricted to a linear map, while auto encoders can have nonlinear enoder/decoders. A single layer auto encoder with linear transfer function is nearly equivalent to PCA, where nearly means that the found by AE and PCA won’t be the same–but the subspace spanned by the respective ‘s will.
What’s the difference between PCA and machine learning?
This is a result of the complexity in representation that arises from composing lower features from lower layers in the network. The currently accepted answer by @bayerj states that the weights of a linear autoencoder span the same subspace as the principal components found by PCA, but they are not the same vectors.
When does auto associative network agree with PCA?
It is true that auto-associate networks with linear activation functions agree with PCA, regardless of the number of hidden layers. However, if there is only 1 hidden layer (input-hidden-output), the optimal auto-associative network still agrees with PCA, even with non-linear activation functions.