B1050
Title: Functional support vector machine
Authors: Todd Ogden - Columbia University (United States) [presenting]
Shanghong Xie - Southwestern University of Finance and Economics (China)
Abstract: Linear and generalized linear scalar-on-function modeling have been commonly used to understand the relationship between a scalar response variable (e.g., continuous, binary outcomes) and functional predictors. Such techniques are sensitive to model misspecification when the relationship between the response variable and the functional predictors is complex. On the other hand, support vector machines (SVMs) are among the most robust prediction models but do not take account of the high correlations between repeated measurements and cannot be used for irregular data. A novel method is proposed to integrate functional principal component analysis (FPCA) with SVM techniques for classification and regression to account for the continuous nature of functional data and the nonlinear relationship between the scalar response variable and the functional predictors. The performance of the method is demonstrated through extensive simulation experiments and through the problem of classification of alcoholics using electroencephalography (EEG) signals.
