Partial label learning based on label distributions and error-correcting output codes

Partial label learning (PLL) is a class of weak supervision learning problems in which each data sample has a candidate set of labels, among which only one label is correct. In this paper, a new PLL algorithm with prior information of the label distribution based on ECOC (PL-PIE) is proposed. PL-PIE utilizes the ECOC framework to decompose the problem into multiple binary problems. Different from the instability of the existing random dichotomy, the proposal exploits the prior information of label distribution to generate positive and negative classes with stable performance. Extensive experimental results demonstrate that the proposed PL-PIE algorithm has highly competitive performance compared to the state-of-the-art PLL algorithms.