  Binary codes can be used to speed up nearest neighbor search tasks in large
scale data sets as they are efficient for both storage and retrieval. In this
paper, we propose a robust auto-encoder model that preserves the geometric
relationships of high-dimensional data sets in Hamming space. This is done by
considering a noise-removing function in a region surrounding the manifold
where the training data points lie. This function is defined with the property
that it projects the data points near the manifold into the manifold wisely,
and we approximate this function by its first order approximation. Experimental
results show that the proposed method achieves better than state-of-the-art
results on three large scale high dimensional data sets.
