  Multi-view learning is a learning problem that utilizes the various
representations of an object to mine valuable knowledge and improve the
performance of learning algorithm, and one of the significant directions of
multi-view learning is sub-space learning. As we known, auto-encoder is a
method of deep learning, which can learn the latent feature of raw data by
reconstructing the input, and based on this, we propose a novel algorithm
called Auto-encoder based Co-training Multi-View Learning (ACMVL), which
utilizes both complementarity and consistency and finds a joint latent feature
representation of multiple views. The algorithm has two stages, the first is to
train auto-encoder of each view, and the second stage is to train a supervised
network. Interestingly, the two stages share the weights partly and assist each
other by co-training process. According to the experimental result, we can
learn a well performed latent feature representation, and auto-encoder of each
view has more powerful reconstruction ability than traditional auto-encoder.
