Title

Novelty Detection Using Sparse Online Gaussian Processes For Visual Object Recognition

Abstract

Gaussian processes (GPs) have been shown to be highly effective for novelty detection through the use of different membership scores. However, applications of GPs to novelty detection have been limited only to batch GP, which require all training data at once and have quadratic space complexity and cubic time complexity. This paper proposes the use of sparse online GP (SOGP) for novelty detection, overcoming these limitations. Our experiments show that SOGP-based novelty detection is capable of achieving performances similar to those from batch GP, even under strong sparseness constraints. Additionally, it is suggested here that membership scores that combine the posterior mean and the posterior variance of the GP might be better fitted to novelty detection than scores leveraging only one of the two posterior moments. Copyright © 2013, Association for the Advancement of Artificial Intelligence. All rights reserved.

Publication Date

12-13-2013

Publication Title

FLAIRS 2013 - Proceedings of the 26th International Florida Artificial Intelligence Research Society Conference

Number of Pages

124-129

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

Socpus ID

84889813958 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84889813958

This document is currently not available here.

Share

COinS