Year: 2025
Type: article
Abstract: We propose a new Bayesian nonparametric prior for latent feature models, called the Convergent Indian Buffet Process (CIBP). We show that under the CIBP, the number of latent features is distributed as a Poisson distribution, with the mean monotonically increasing but converging to a certain value as the number of objects goes to infinity. That is, the expected number of features is bounded above even when the number of objects goes to infinity, unlike the standard Indian Buffet Process, under which the expected number of features increases with the number of objects. We provide two alternative representations of the CIBP based on a hierarchical distribution and a completely random measure, which are of independent interest. The proposed CIBP is assessed on a high-dimensional sparse factor model.
Source: Mathematics
DOI: 10.3390/math13233881
Author: Ilsang Ohn
Institution: Inha University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,137
Funder: Inha University
OpenAlex ID: W7108328228
FWCI:
Cites:
Cited by:
Related to: