Discovering Homogeneous Regions in Spatial Data through Competition

If all features causing heterogeneity were observed, a mixture of experts approach (Jacobs et al., 1991) is likely to be superior to using a single model. When unobserved or very noisy spatial features are the cause for the heterogeneity, the observed feature spaces of homogeneous subsets can highly overlap, leading to a biased global model or biased mixture of experts. Our goal is to allow more accurate predictions in such situations. Here, a supervised machine learning algorithm for the analysis of heterogeneous spatial data is proposed. It is based on partitioning the data set into more homogeneous regions by competition of regression models (linear or nonlinear). The algorithm starts from learning a global model, and adds new models into the competition until each model becomes specialized for one of the regions. The competition convergence is proven theoretically. Also, the influence of filtering the competing models residuals for improving convergence speed and accuracy is discus...

• Type: Conference
• Year: 2000
• Where: ICML
• Authors: Slobodan Vucetic, Zoran Obradovic
• Keywords: Heterogeneous Spatial Data, ICML 2000, Machine Learning, Real-life Spatial Data, Spatial Data Sets