Regularized modal regression with data-dependent hypothesis spaces

Modal regression aims at learning the conditional mode function, which is different from the traditional least-squares for approximating the conditional mean function. Due to its robust to complex noise and outliers, modal regression has attracted increasing attention recently in statistics and machine learning community. However, most of the previous modal regression models are limited to learning framework with data-independent hypothesis spaces. Usually, the data-dependent hypothesis spaces can provide much flexibility and adaptivity for many learning problems. By employing data-dependent hypothesis spaces, we propose a new regularized modal regression and establish its generalization error analysis. Data experiments demonstrate the competitive performance of the proposed model over the related least-squares regression.