HERA: Partial Label Learning by Combining Heterogeneous Loss with Sparse and Low-Rank Regularization
This addresses the challenge of learning from ambiguous labels in machine learning, which is incremental as it builds on existing PLL methods by integrating new loss functions and regularization.
The paper tackles the problem of Partial Label Learning (PLL), where each training instance has multiple candidate labels with only one correct, by proposing HERA, a method that combines heterogeneous loss with sparse and low-rank regularization to estimate labeling confidence. It achieves superior or comparable performance to state-of-the-art methods in experiments on artificial and real-world datasets.
Partial Label Learning (PLL) aims to learn from the data where each training instance is associated with a set of candidate labels, among which only one is correct. Most existing methods deal with such problem by either treating each candidate label equally or identifying the ground-truth label iteratively. In this paper, we propose a novel PLL approach called HERA, which simultaneously incorporates the HeterogEneous Loss and the SpaRse and Low-rAnk procedure to estimate the labeling confidence for each instance while training the model. Specifically, the heterogeneous loss integrates the strengths of both the pairwise ranking loss and the pointwise reconstruction loss to provide informative label ranking and reconstruction information for label identification, while the embedded sparse and low-rank scheme constrains the sparsity of ground-truth label matrix and the low rank of noise label matrix to explore the global label relevance among the whole training data for improving the learning model. Extensive experiments on both artificial and real-world data sets demonstrate that our method can achieve superior or comparable performance against the state-of-the-art methods.