Redundant Label Learning via Subspace Representation and Global Disambiguation

Abstract

Redundant Label Learning (RLL) aims at inducing a robust model from training data, where each example is associated with a set of candidate labels, among which some of them are incorrect. Most existing approaches deal with such problem by disambiguating the candidate labels first and then inducing the predictive model from the disambiguated data. However, these approaches only focus on disambiguation for each instance’ candidate label set, while the global label context tends to be ignored. Meanwhile, these approaches usually induce the objective model by directly utilizing the original feature information, which may lead to the model overfitting due to high-dimensional redundant features. To tackle the above issues, we propose a novel feature S ubspac E R epresentation and label G lobal Disambiguat IO n ( SERGIO ) approach, which improves the generalization ability of the learning system from the perspective of both feature space and label space. Specifically, we project the original high-dimensional feature space into a low-dimensional subspace, where the projection matrix is regularized with an orthogonality constraint to make the subspace more compact. Meanwhile, we introduce a label confidence matrix and constrain it with ℓ 1 -norm and trace-norm regularization simultaneously, which are utilized to explore global label correlations and further well in accordance with the nature of single-label classification and multi-label classification problem, respectively. Extensive experiments on both single-label and multi-label RLL datasets demonstrate that our proposed method achieves competitive performance against state-of-the-art approaches.