Exploring Binary Classification Hidden within Partial Label Learning

Partial label learning (PLL) is to learn a discriminative model under incomplete supervision, where each instance is annotated with a candidate label set. The basic principle of PLL is that the unknown correct label y of an instance x resides in its candidate label set s, i. e. , P(y ∈ s | x) = 1. On which basis, current researches either directly model P(x | y) under different data generation assumptions or propose various surrogate multiclass losses, which all aim to encourage the model-based Pθ(y ∈ s | x)→1 implicitly. In this work, instead, we explicitly construct a binary classification task toward P(y ∈ s | x) based on the discriminative model, that is to predict whether the model-output label of x is one of its candidate labels. We formulate a novel risk estimator with estimation error bound for the proposed PLL binary classification risk. By applying logit adjustment based on disambiguation strategy, the practical approach directly maximizes Pθ(y ∈ s | x) while implicitly disambiguating the correct one from candidate labels simultaneously. Thorough experiments validate that the proposed approach achieves competitive performance against the state-of-the-art PLL methods.

Authors

• Hengheng Luo
• Yabin ZHANG
• Suyun Zhao Key Lab of Data Engineering and Knowledge Engineering of MOE Renmin University of China School of Information, Renmin University of China
• Hong Chen
• Cuiping Li Key Lab of Data Engineering and Knowledge Engineering of MOE Renmin University of China School of Information, Renmin University of China

Keywords

• Machine Learning: Classification
• Machine Learning: Weakly Supervised Learning