@article{10.1007/s10618-018-0564-z,
author = {Liang, Jian and Chen, Kun and Lin, Ming and Zhang, Changshui and Wang, Fei},
title = {Robust Finite Mixture Regression for Heterogeneous Targets},
year = {2018},
issue_date = {November  2018},
publisher = {Kluwer Academic Publishers},
address = {USA},
volume = {32},
number = {6},
issn = {1384-5810},
url = {https://doi.org/10.1007/s10618-018-0564-z},
doi = {10.1007/s10618-018-0564-z},
abstract = {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.},
journal = {Data Min. Knowl. Discov.},
month = nov,
pages = {1509–1560},
numpages = {52},
keywords = {Task clustering, Finite Mixture Regression, Mixed-type response, Anomaly detection, Incomplete targets}
}