Correlated Non-Parametric Latent Feature Models

TL;DR

This paper introduces a framework for correlated nonparametric latent feature models, extending the Indian Buffet Process to account for feature correlations, and demonstrates its effectiveness on real-world data.

Contribution

It generalizes the IBP to model correlated features, providing a flexible framework for real-world applications where features are not independent.

Findings

Framework successfully models correlated features

Demonstrated on real-world datasets

Outperforms uncorrelated models in capturing data structure

Abstract

We are often interested in explaining data through a set of hidden factors or features. When the number of hidden features is unknown, the Indian Buffet Process (IBP) is a nonparametric latent feature model that does not bound the number of active features in dataset. However, the IBP assumes that all latent features are uncorrelated, making it inadequate for many realworld problems. We introduce a framework for correlated nonparametric feature models, generalising the IBP. We use this framework to generate several specific models and demonstrate applications on realworld datasets.