The Supervised IBP: Neighbourhood Preserving Infinite Latent Feature Models

Understand

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.