Robust Finite Mixture Regression for Heterogeneous Targets
This work addresses the challenge of sample heterogeneity in regression for domains like healthcare or finance, but it appears incremental as it builds on existing finite mixture regression frameworks with added robustness features.
The paper tackles the problem of modeling heterogeneous data with multiple regression tasks by proposing a robust finite mixture regression model that handles mixed-type targets, shared feature selection, and anomaly detection, achieving state-of-the-art performance on synthetic and real-world datasets.
Finite Mixture Regression (FMR) refers to the mixture modeling scheme which learns multiple regression models from the training data set. Each of them is in charge of a subset. FMR is an effective scheme for handling sample heterogeneity, where a single regression model is not enough for capturing the complexities of the conditional distribution of the observed samples given the features. In this paper, we propose an FMR model that 1) finds sample clusters and jointly models multiple incomplete mixed-type targets simultaneously, 2) achieves shared feature selection among tasks and cluster components, and 3) detects anomaly tasks or clustered structure among tasks, and accommodates outlier samples. We provide non-asymptotic oracle performance bounds for our model under a high-dimensional learning framework. The proposed model is evaluated on both synthetic and real-world data sets. The results show that our model can achieve state-of-the-art performance.