Abstract
Nonlinear regression is a common statistical tool to
solve many computer vision problems (e.g., age estimation,
pose estimation). Existing approaches to nonlinear regression fall into two main categories: (1) The universal approach provides an implicit or explicit homogeneous feature
mapping (e.g., kernel ridge regression, Gaussian process
regression, neural networks). These approaches may fail
when data is heterogeneous or discontinuous. (2) Divideand-conquer approaches partition a heterogeneous input
feature space and learn multiple local regressors. However, existing divide-and-conquer approaches fail to deal
with discontinuities between partitions (e.g., Gaussian mixture of regressions) and they cannot guarantee that the partitioned input space will be homogeneously modeled by local regressors (e.g., ordinal regression). To address these
issues, this paper proposes Soft-Margin Mixture of Regressions (SMMR), a method that directly learns homogeneous
partitions of the input space and is able to deal with discontinuities. SMMR outperforms the state-of-the-art methods on three popular computer vision tasks: age estimation,
crowd counting and viewpoint estimation from images