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Robust finite mixture regression for heterogeneous targets

Published: 01 November 2018 Publication History

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.

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  • (2024)The finite mixture model for the tails of distributionStatistical Analysis and Data Mining10.1002/sam.1167117:2Online publication date: 27-Mar-2024

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Information & Contributors

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Published In

cover image Data Mining and Knowledge Discovery
Data Mining and Knowledge Discovery  Volume 32, Issue 6
November 2018
336 pages

Publisher

Kluwer Academic Publishers

United States

Publication History

Published: 01 November 2018

Author Tags

  1. Anomaly detection
  2. Finite Mixture Regression
  3. Incomplete targets
  4. Mixed-type response
  5. Task clustering

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Cited By

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  • (2024)The finite mixture model for the tails of distributionStatistical Analysis and Data Mining10.1002/sam.1167117:2Online publication date: 27-Mar-2024

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