TY - CONF
AB - 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.
AU - Quadrianto, Novi
AU - Sharmanska, Viktoriia
AU - Knowles, David
AU - Ghahramani, Zoubin
ID - 2520
SN - 9780974903996
T2 - Proceedings of the 29th conference uncertainty in Artificial Intelligence
TI - The supervised IBP: Neighbourhood preserving infinite latent feature models
ER -