Classification using log Gaussian Cox processes

by   Alexander G. de G. Matthews, et al.
University of Cambridge

McCullagh and Yang (2006) suggest a family of classification algorithms based on Cox processes. We further investigate the log Gaussian variant which has a number of appealing properties. Conditioned on the covariates, the distribution over labels is given by a type of conditional Markov random field. In the supervised case, computation of the predictive probability of a single test point scales linearly with the number of training points and the multiclass generalization is straightforward. We show new links between the supervised method and classical nonparametric methods. We give a detailed analysis of the pairwise graph representable Markov random field, which we use to extend the model to semi-supervised learning problems, and propose an inference method based on graph min-cuts. We give the first experimental analysis on supervised and semi-supervised datasets and show good empirical performance.


page 4

page 10


Algorithms for ℓ_p-based semi-supervised learning on graphs

We develop fast algorithms for solving the variational and game-theoreti...

Infinitely Wide Graph Convolutional Networks: Semi-supervised Learning via Gaussian Processes

Graph convolutional neural networks (GCNs) have recently demonstrated pr...

Explicit Pairwise Factorized Graph Neural Network for Semi-Supervised Node Classification

Node features and structural information of a graph are both crucial for...

Semi-supervised Learning Meets Factorization: Learning to Recommend with Chain Graph Model

Recently latent factor model (LFM) has been drawing much attention in re...

Semi-supervised Predictive Clustering Trees for (Hierarchical) Multi-label Classification

Semi-supervised learning (SSL) is a common approach to learning predicti...

Accelerating System Log Processing by Semi-supervised Learning: A Technical Report

There is an increasing need for more automated system-log analysis tools...

Hierarchical Semi-Markov Conditional Random Fields for Recursive Sequential Data

Inspired by the hierarchical hidden Markov models (HHMM), we present the...

Please sign up or login with your details

Forgot password? Click here to reset