Autoencoder: Difference between revisions

Revision as of 10:54, 19 September 2023

Revision as of 12:09, 3 November 2023

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=== Dimensionality reduction ===

[[File:PCA vs Linear Autoencoder.png|thumb|Plot of the first two Principal Components (left) and a two-dimension hidden layer of a Linear Autoencoder (Right) applied to the [[Fashion MNIST dataset]].<ref name=":10">{{Cite web|url=https://github.com/zalandoresearch/fashion-mnist|title=Fashion MNIST|website=[[GitHub]]|date=2019-07-12}}</ref> The two models being both linear learn to span the same subspace. The projection of the data points is indeed identical, apart from rotation of the subspace. -While PCA selects a specific orientation up to whichreflections PCAin the general case, the cost function of a simple autoencoder is invariant to rotations of the latent space.]][[Dimensionality reduction]] was one of the first [[deep learning]] applications.<ref name=":0" />

For Hinton's 2006 study,<ref name=":7" /> he pretrained a multi-layer autoencoder with a stack of [[Restricted Boltzmann machine|RBMs]] and then used their weights to initialize a deep autoencoder with gradually smaller hidden layers until hitting a bottleneck of 30 neurons. The resulting 30 dimensions of the code yielded a smaller reconstruction error compared to the first 30 components of a principal component analysis (PCA), and learned a representation that was qualitatively easier to interpret, clearly separating data clusters.<ref name=":0" /><ref name=":7" />