# Scaling the Indian Buffet Process via Submodular Maximization

arXiv: https://arxiv.org/abs/1304.3285 · HTML: https://mlsanity.com/paper/1304.3285/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Apr 2013.

## Built on by (most cited first, 1 of 1)

- [VICE: Variational Interpretable Concept Embeddings](https://mlsanity.com/paper/2205.00756/) (2022)

## Abstract (arXiv)

Inference for latent feature models is inherently difficult as the inference space grows exponentially with the size of the input data and number of latent features. In this work, we use Kurihara & Welling (2008)'s maximization-expectation framework to perform approximate MAP inference for linear-Gaussian latent feature models with an Indian Buffet Process (IBP) prior. This formulation yields a submodular function of the features that corresponds to a lower bound on the model evidence. By adding a constant to this function, we obtain a nonnegative submodular function that can be maximized via a greedy algorithm that obtains at least a one-third approximation to the optimal solution. Our inference method scales linearly with the size of the input data, and we show the efficacy of our method on the largest datasets currently analyzed using an IBP model.