关键词: 弱监督学习, 消歧, 纠错输出编码, 偏标记学习

Abstract: Partial label learning is an important weakly supervised learning framework. In partial label learning, each training example is associated with a set of candidate labels, among which only one is valid. Obviously, the more the candidate labels, the greater the difficulty of partial label learning. In order to decrease the number of candidate labels to reduce the difficulty of partial label learning, this paper proposes a ternary error-correcting output codes based partial label learning algorithm (PL-TECOC) which transforms a partial label learning problem into a series of binary class learning problems and integrates these binary classifiers finally. PL-TECOC utilizes “0” coding to ignore the corresponding label and constructs the positive and negative classes only based on non-zero coding in the construction process of binary class training data. Compared with several popular partial label learning algorithms, experimental results on artificial and real-world datasets show good performance of PL-TECOC.

Key words: weakly supervised learning, disambiguation, error-correcting output codes, partial label learning