|Author||Quadrianto, Novi ♦ Sharmanska, Viktoriia ♦ Knowles, David A. ♦ Ghahramani, Zoubin|
|Subject Domain (in DDC)||Computer science, information & general works ♦ Data processing & computer science|
|Description||We propose a probabilistic model to infer supervised latent variables in the Hamming space from observed data. Our model al-lows 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 con-cepts have dissimilar latent values. We for-mulate 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 cou-ple the structure of the hash codes with con-tinuously growing structure of the neighbour-hood preserving infinite latent feature space. 1
|Educational Role||Student ♦ Teacher|
|Age Range||above 22 year|
|Education Level||UG and PG ♦ Career/Technical Study|
|Learning Resource Type||Article|
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