Large scale multi-output multi-class classification using Gaussian processes

Chunchao Ma, MA Alvarez

Research output: Contribution to journalArticlepeer-review

Abstract

Multi-output Gaussian processes (MOGPs) can help to improve predictive performance for some output variables, by leveraging the correlation with other output variables. In this paper, our main motivation is to use multiple-output Gaussian processes to exploit correlations between outputs where each output is a multi-class classification problem. MOGPs have been mostly used for multi-output regression. There are some existing works that use MOGPs for other types of outputs, e.g., multi-output binary classification. However, MOGPs for multi-class classification has been less studied. The reason is twofold: 1) when using a softmax function, it is not clear how to scale it beyond the case of a few outputs; 2) most common type of data in multi-class classification problems consists of image data, and MOGPs are not specifically designed to image data. We thus propose a new MOGPs model called Multi-output Gaussian Processes with Augment & Reduce (MOGPs-AR) that can deal with large scale classification and downsized image input data. Large scale classification is achieved by subsampling both training data sets and classes in each output whereas downsized image input data is handled by incorporating a convolutional kernel into the new model. We show empirically that our proposed model outperforms single-output Gaussian processes in terms of different performance metrics and multi-output Gaussian processes in terms of scalability, both in synthetic and in real classification problems. We include an example with the Ommiglot dataset where we showcase the properties of our model.

Original languageEnglish
Pages (from-to)1077-1106
Number of pages30
JournalMachine Learning
Volume112
Issue number4
Early online date8 Feb 2023
DOIs
Publication statusPublished - 1 Apr 2023

Keywords

  • Classification
  • Gaussian processes
  • Image data
  • Multi-output Gaussian processes
  • Transfer learning

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