Block-GP: Scalable Gaussian Process Regression for Multimodal Data

Regression problems on massive data sets are ubiquitous in many application domains including the Internet, earth and space sciences, and finances. In many cases, regression algorithms such as linear regression or neural networks attempt to fit the target variable as a function of the input variables without regard to the underlying joint distribution of the variables. As a result, these global models are not sensitive to variations in the local structure of the input space. Several algorithms, including the mixture of experts model, classification and regression trees (CART), and others have been developed, motivated by the fact that a variability in the local distribution of inputs may be reflective of a significant change in the target variable. While these methods can handle the non-stationarity in the relationships to varying degrees, they are often not scalable and, therefore, not used in large scale data mining applications. In this paper we develop Block-GP, a Gaussian Process ...

• Type: Journal
• Year: 2010
• Where: ICDM
• Authors: Kamalika Das, Ashok N. Srivastava
• Keywords: Data Mining, ICDM 2010, Input Space, Nonlinear Regression, Regression Algorithms