Spectral Methods for Indian Buffet Process Inference
Abstract
The Indian Buffet Process is a versatile statistical tool for modeling distributions over binary matrices. We provide an efficient spectral algorithm as an alternative to costly Variational Bayes and sampling-based algorithms. We derive a novel tensorial characterization of the moments of the Indian Buffet Process proper and for two of its applications. We give a computationally efficient iterative inference algorithm, concentration of measure bounds, and reconstruction guarantees. Our algorithm provides superior accuracy and cheaper computation than comparable Variational Bayesian approach on a number of reference problems.
Cite
Text
Tung and Smola. "Spectral Methods for Indian Buffet Process Inference." Neural Information Processing Systems, 2014.Markdown
[Tung and Smola. "Spectral Methods for Indian Buffet Process Inference." Neural Information Processing Systems, 2014.](https://mlanthology.org/neurips/2014/tung2014neurips-spectral/)BibTeX
@inproceedings{tung2014neurips-spectral,
title = {{Spectral Methods for Indian Buffet Process Inference}},
author = {Tung, Hsiao-Yu and Smola, Alexander J},
booktitle = {Neural Information Processing Systems},
year = {2014},
pages = {1484-1492},
url = {https://mlanthology.org/neurips/2014/tung2014neurips-spectral/}
}