The Supervised IBP: Neighbourhood Preserving Infinite Latent Feature Models

Novi Quadrianto, Viktoriia Sharmanska, David A. Knowles, Zoubin Ghahramani
Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence, PMLR R11:608-617, 2013.

Abstract

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR11-quadrianto13a, title = {The Supervised {IBP}: Neighbourhood Preserving Infinite Latent Feature Models}, author = {Quadrianto, Novi and Sharmanska, Viktoriia and Knowles, David A. and Ghahramani, Zoubin}, booktitle = {Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence}, pages = {608--617}, year = {2013}, editor = {Nicholson, Ann and Smyth, Padhraic}, volume = {R11}, series = {Proceedings of Machine Learning Research}, month = {12--14 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r11/main/assets/quadrianto13a/quadrianto13a.pdf}, url = {https://proceedings.mlr.press/r11/quadrianto13a.html}, abstract = {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.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T The Supervised IBP: Neighbourhood Preserving Infinite Latent Feature Models %A Novi Quadrianto %A Viktoriia Sharmanska %A David A. Knowles %A Zoubin Ghahramani %B Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2013 %E Ann Nicholson %E Padhraic Smyth %F pmlr-vR11-quadrianto13a %I PMLR %P 608--617 %U https://proceedings.mlr.press/r11/quadrianto13a.html %V R11 %X 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. %Z Reissued by PMLR on 04 October 2026.
APA
Quadrianto, N., Sharmanska, V., Knowles, D.A. & Ghahramani, Z.. (2013). The Supervised IBP: Neighbourhood Preserving Infinite Latent Feature Models. Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R11:608-617 Available from https://proceedings.mlr.press/r11/quadrianto13a.html. Reissued by PMLR on 04 October 2026.

Related Material