Title: Reconstruction of Hidden Representation for Robust Feature Extraction,
Abstract: This paper aims to develop a new and robust approach to feature
representation. Motivated by the success of Auto-Encoders, we first theoretical
summarize the general properties of all algorithms that are based on
traditional Auto-Encoders: 1) The reconstruction error of the input can not be
lower than a lower bound, which can be viewed as a guiding principle for
reconstructing the input. Additionally, when the input is corrupted with
noises, the reconstruction error of the corrupted input also can not be lower
than a lower bound. 2) The reconstruction of a hidden representation achieving
its ideal situation is the necessary condition for the reconstruction of the
input to reach the ideal state. 3) Minimizing the Frobenius norm of the
Jacobian matrix of the hidden representation has a deficiency and may result in
a much worse local optimum value. We believe that minimizing the reconstruction
error of the hidden representation is more robust than minimizing the Frobenius
norm of the Jacobian matrix of the hidden representation. Based on the above
analysis, we propose a new model termed Double Denoising Auto-Encoders (DDAEs),
which uses corruption and reconstruction on both the input and the hidden
representation. We demonstrate that the proposed model is highly flexible and
extensible and has a potentially better capability to learn invariant and
robust feature representations. We also show that our model is more robust than
Denoising Auto-Encoders (DAEs) for dealing with noises or inessential features.
Furthermore, we detail how to train DDAEs with two different pre-training
methods by optimizing the objective function in a combined and separate manner,
respectively. Comparative experiments illustrate that the proposed model is
significantly better for representation learning than the state-of-the-art
models.