Saddlepoints in Unsupervised Least Squares
This provides theoretical insight into optimization challenges in auto-encoders, which is incremental for researchers in unsupervised learning and neural network theory.
The paper tackles the risk landscape of unsupervised least squares in deep auto-encoding neural nets, establishing an equivalence with principal manifolds and showing that all non-trivial critical points are saddlepoints, with degenerate cases in overcomplete auto-encoding.
This paper sheds light on the risk landscape of unsupervised least squares in the context of deep auto-encoding neural nets. We formally establish an equivalence between unsupervised least squares and principal manifolds. This link provides insight into the risk landscape of auto--encoding under the mean squared error, in particular all non-trivial critical points are saddlepoints. Finding saddlepoints is in itself difficult, overcomplete auto-encoding poses the additional challenge that the saddlepoints are degenerate. Within this context we discuss regularization of auto-encoders, in particular bottleneck, denoising and contraction auto-encoding and propose a new optimization strategy that can be framed as particular form of contractive regularization.