Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Distance Dependent Infinite Latent Feature Models   Latent feature models are widely used to decompose data into a small number
of components. Bayesian nonparametric variants of these models, which use the
Indian buffet process (IBP) as a prior over latent features, allow the number
of features to be determined from the data. We present a generalization of the
IBP, the distance dependent Indian buffet process (dd-IBP), for modeling
non-exchangeable data. It relies on distances defined between data points,
biasing nearby data to share more features. The choice of distance measure
allows for many kinds of dependencies, including temporal and spatial. Further,
the original IBP is a special case of the dd-IBP. In this paper, we develop the
dd-IBP and theoretically characterize its feature-sharing properties. We derive
a Markov chain Monte Carlo sampler for a linear Gaussian model with a dd-IBP
prior and study its performance on several non-exchangeable data sets.

21
The categories of this paper are: adap-org
15
The categories of this paper are: nucl-ex
24
The categories of this paper are: dg-ga
10
The categories of this paper are: q-bio
19
The categories of this paper are: eess
5
The categories of this paper are: gr-qc
11
The categories of this paper are: hep-lat
23
The categories of this paper are: solv-int
34
The categories of this paper are: chem-ph
36
The categories of this paper are: cmp-lg
37
The categories of this paper are: supr-con
31
The categories of this paper are: ao-sci
13
The categories of this paper are: quant-ph
29
The categories of this paper are: funct-an
0
The categories of this paper are: hep-ph
22
The categories of this paper are: chao-dyn