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Latent Supervised Learning

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  • Susan Wei
  • Michael R. Kosorok

Abstract

This article introduces a new machine learning task, called latent supervised learning, where the goal is to learn a binary classifier from continuous training labels that serve as surrogates for the unobserved class labels. We investigate a specific model where the surrogate variable arises from a two-component Gaussian mixture with unknown means and variances, and the component membership is determined by a hyperplane in the covariate space. The estimation of the separating hyperplane and the Gaussian mixture parameters forms what shall be referred to as the change-line classification problem. We propose a data-driven sieve maximum likelihood estimator for the hyperplane, which in turn can be used to estimate the parameters of the Gaussian mixture. The estimator is shown to be consistent. Simulations as well as empirical data show the estimator has high classification accuracy.

Suggested Citation

  • Susan Wei & Michael R. Kosorok, 2013. "Latent Supervised Learning," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(503), pages 957-970, September.
  • Handle: RePEc:taf:jnlasa:v:108:y:2013:i:503:p:957-970
    DOI: 10.1080/01621459.2013.789695
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    Cited by:

    1. Zhang, Xiaochen & Zhang, Qingzhao & Ma, Shuangge & Fang, Kuangnan, 2022. "Subgroup analysis for high-dimensional functional regression," Journal of Multivariate Analysis, Elsevier, vol. 192(C).
    2. Atul Mallik & Moulinath Banerjee & George Michailidis, 2020. "M-estimation in Multistage Sampling Procedures," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 82(2), pages 261-309, August.
    3. Cai, Tingting & Li, Jianbo & Zhou, Qin & Yin, Songlou & Zhang, Riquan, 2024. "Subgroup detection based on partially linear additive individualized model with missing data in response," Computational Statistics & Data Analysis, Elsevier, vol. 192(C).
    4. Shao, Lihui & Wu, Jiaqi & Zhang, Weiping & Chen, Yu, 2024. "Integrated subgroup identification from multi-source data," Computational Statistics & Data Analysis, Elsevier, vol. 193(C).

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