Title: From Principal Subspaces to Principal Components with Linear Autoencoders,
Abstract: The autoencoder is an effective unsupervised learning model which is widely
used in deep learning. It is well known that an autoencoder with a single
fully-connected hidden layer, a linear activation function and a squared error
cost function trains weights that span the same subspace as the one spanned by
the principal component loading vectors, but that they are not identical to the
loading vectors. In this paper, we show how to recover the loading vectors from
the autoencoder weights.