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Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms

Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms

Published on: February 8, 2019

Latent Supervised Learning.

Susan Wei¹, Michael R Kosorok

¹Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599.

Journal of the American Statistical Association

|December 10, 2013

Summary

This summary is machine-generated.

This study introduces latent supervised learning for binary classification using continuous surrogate labels. A novel change-line classification method demonstrates high accuracy in estimating underlying class labels.

Keywords:

Classification and Clustering Glivenko-Cantelli classes Sieve Maximum Likelihood Estimation Sliced Inverse Regression Statistical Learning

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Area of Science:

Machine Learning
Statistical Modeling
Pattern Recognition

Background:

Learning binary classifiers often requires discrete class labels.
Continuous labels can serve as surrogates for unobserved discrete labels in machine learning tasks.
Existing methods may struggle with inferring discrete labels from continuous surrogates.

Purpose of the Study:

Introduce latent supervised learning, a new machine learning task.
Develop a model for binary classification using continuous training labels.
Address the challenge of estimating unobserved class labels from surrogate data.

Main Methods:

Investigate a model where surrogate variables arise from a two-component Gaussian mixture.
Define the change-line classification problem for estimating hyperplane and mixture parameters.
Propose a data-driven sieve maximum likelihood estimator for the separating hyperplane.

Main Results:

The proposed estimator is shown to be consistent.
The estimator effectively estimates Gaussian mixture parameters.
Simulations and empirical data confirm high classification accuracy.

Conclusions:

Latent supervised learning offers a viable approach for binary classification with continuous surrogate labels.
The change-line classification model and sieve maximum likelihood estimator are effective.
The proposed method achieves high accuracy in inferring unobserved class labels.