Higher-order Multivariable Polynomial Regression to Estimate Human Affective States.

ABSTRACT: From direct observations, facial, vocal, gestural, physiological, and central nervous signals, estimating human affective states through computational models such as multivariate linear-regression analysis, support vector regression, and artificial neural network, have been proposed in the past decade. In these models, linear models are generally lack of precision because of ignoring intrinsic nonlinearities of complex psychophysiological processes; and nonlinear models commonly adopt complicated algorithms. To improve accuracy and simplify model, we introduce a new computational modeling method named as higher-order multivariable polynomial regression to estimate human affective states. The study employs standardized pictures in the International Affective Picture System to induce thirty

SUBMITTER: Wei J 

PROVIDER: S-EPMC4800309 | biostudies-literature | 2016 Mar

REPOSITORIES: biostudies-literature