---
res:
bibo_abstract:
- "We propose a probabilistic model to infer supervised latent variables in\r\nthe
Hamming space from observed data. Our model allows simultaneous\r\ninference of
the number of binary latent variables, and their values. The\r\nlatent variables
preserve neighbourhood structure of the data in a sense\r\nthat objects in the
same semantic concept have similar latent values, and\r\nobjects in different
concepts have dissimilar latent values. We formulate\r\nthe supervised infinite
latent variable problem based on an intuitive\r\nprinciple of pulling objects
together if they are of the same type, and\r\npushing them apart if they are not.
We then combine this principle with a\r\nflexible Indian Buffet Process prior
on the latent variables. We show that\r\nthe inferred supervised latent variables
can be directly used to perform a\r\nnearest neighbour search for the purpose
of retrieval. We introduce a new\r\napplication of dynamically extending hash
codes, and show how to\r\neffectively couple the structure of the hash codes with
continuously\r\ngrowing structure of the neighbourhood preserving infinite latent
feature\r\nspace.@eng"
bibo_authorlist:
- foaf_Person:
foaf_givenName: Novi
foaf_name: Quadrianto, Novi
foaf_surname: Quadrianto
- foaf_Person:
foaf_givenName: Viktoriia
foaf_name: Sharmanska, Viktoriia
foaf_surname: Sharmanska
foaf_workInfoHomepage: http://www.librecat.org/personId=2EA6D09E-F248-11E8-B48F-1D18A9856A87
- foaf_Person:
foaf_givenName: David
foaf_name: Knowles, David
foaf_surname: Knowles
- foaf_Person:
foaf_givenName: Zoubin
foaf_name: Ghahramani, Zoubin
foaf_surname: Ghahramani
dct_date: 2013^xs_gYear
dct_isPartOf:
- http://id.crossref.org/issn/9780974903996
dct_language: eng
dct_publisher: AUAI Press@
dct_title: 'The supervised IBP: Neighbourhood preserving infinite latent feature
models@'
...