Large-scale Gaussian process classification using random decision forests

Gaussian processes are powerful modeling tools in machine learning which offer wide applicability for regression and classification tasks due to their non-parametric and non-linear behavior. However, one of their main drawbacks is the training time complexity which scales cubically with the number of examples. Our work addresses this issue by combining Gaussian processes with random decision forests to enable fast learning. An important advantage of our method is its simplicity and the ability to directly control the tradeoff between classification performance and computational speed. Experiments on an indoor place recognition task and on standard machine learning benchmarks show that our method can handle large training sets of up to three million examples in reasonable time while retaining good classification accuracy.