Switched linear encoding with rectified linear autoencoders
This work offers incremental insights into the connections between autoencoders and other coding models, primarily for researchers in machine learning theory.
The paper explores a rectified linear autoencoder model to unify sparse linear coding models, providing an intuitive interpretation and demonstrating it on small artificial datasets with known distributions.
Several recent results in machine learning have established formal connections between autoencoders---artificial neural network models that attempt to reproduce their inputs---and other coding models like sparse coding and K-means. This paper explores in depth an autoencoder model that is constructed using rectified linear activations on its hidden units. Our analysis builds on recent results to further unify the world of sparse linear coding models. We provide an intuitive interpretation of the behavior of these coding models and demonstrate this intuition using small, artificial datasets with known distributions.