  Learning based hashing methods have attracted considerable attention due to
their ability to greatly increase the scale at which existing algorithms may
operate. Most of these methods are designed to generate binary codes that
preserve the Euclidean distance in the original space. Manifold learning
techniques, in contrast, are better able to model the intrinsic structure
embedded in the original high-dimensional data. The complexity of these models,
and the problems with out-of-sample data, have previously rendered them
unsuitable for application to large-scale embedding, however. In this work, we
consider how to learn compact binary embeddings on their intrinsic manifolds.
In order to address the above-mentioned difficulties, we describe an efficient,
inductive solution to the out-of-sample data problem, and a process by which
non-parametric manifold learning may be used as the basis of a hashing method.
Our proposed approach thus allows the development of a range of new hashing
techniques exploiting the flexibility of the wide variety of manifold learning
approaches available. We particularly show that hashing on the basis of t-SNE .
