Bayesian Nonparametrics for Non-exhaustive Learning

TL;DR

This paper introduces a Bayesian nonparametric approach for non-exhaustive learning, enabling models to adaptively grow in complexity to handle non-stationary environments with incomplete class information.

Contribution

It proposes a novel doubly non-parametric Bayesian Gaussian mixture model tailored for non-exhaustive learning, addressing limitations of traditional fixed-model methods.

Findings

Demonstrates promising performance in non-exhaustive learning tasks.

Shows adaptability to non-stationary environments.

Handles an arbitrarily large number of classes and components.

Abstract

Non-exhaustive learning (NEL) is an emerging machine-learning paradigm designed to confront the challenge of non-stationary environments characterized by anon-exhaustive training sets lacking full information about the available classes.Unlike traditional supervised learning that relies on fixed models, NEL utilizes self-adjusting machine learning to better accommodate the non-stationary nature of the real-world problem, which is at the root of many recently discovered limitations of deep learning. Some of these hurdles led to a surge of interest in several research areas relevant to NEL such as open set classification or zero-shot learning. The presented study which has been motivated by two important applications proposes a NEL algorithm built on a highly flexible, doubly non-parametric Bayesian Gaussian mixture model that can grow arbitrarily large in terms of the number of classes and their components. We report several experiments that demonstrate the promising performance of the introduced model for NEL.