  We propose a probabilistic model to infer supervised latent variables in the
Hamming space from observed data. Our model allows simultaneous inference of
the number of binary latent variables, and their values. The latent variables
preserve neighbourhood structure of the data in a sense that objects in the
same semantic concept have similar latent values, and objects in different
concepts have dissimilar latent values. We formulate the supervised infinite
latent variable problem based on an intuitive principle of pulling objects
together if they are of the same type, and pushing them apart if they are not.
We then combine this principle with a flexible Indian Buffet Process prior on
the latent variables. We show that the inferred supervised latent variables can
be directly used to perform a nearest neighbour search for the purpose of
retrieval. We introduce a new application of dynamically extending hash codes,
and show how to effectively couple the structure of the hash codes with
continuously growing structure of the neighbourhood preserving infinite latent
feature space.
