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Progressive Identification of True Labels for Partial-Label Learning
Jiaqi Lv · Miao Xu · LEI FENG · Gang Niu · Xin Geng · Masashi Sugiyama

Thu Jul 16 08:00 AM -- 08:45 AM & Thu Jul 16 07:00 PM -- 07:45 PM (PDT) @

Partial-label learning (PLL) is a typical weakly supervised learning problem, where each training instance is equipped with a set of candidate labels among which only one is the true label. Most existing methods elaborately designed learning objectives as constrained optimizations that must be solved in specific manners, making their computational complexity a bottleneck for scaling up to big data. The goal of this paper is to propose a novel framework of PLL with flexibility on the model and optimization algorithm. More specifically, we propose a novel estimator of the classification risk, theoretically analyze the classifier-consistency, and establish an estimation error bound. Then we propose a progressive identification algorithm for approximately minimizing the proposed risk estimator, where the update of the model and identification of true labels are conducted in a seamless manner. The resulting algorithm is model-independent and loss-independent, and compatible with stochastic optimization. Thorough experiments demonstrate it sets the new state of the art.

Author Information

Jiaqi Lv (Southeast University)
Miao Xu (University of Queensland/ RIKEN AIP)
LEI FENG (Nanyang Technological University)
Gang Niu (RIKEN)
Gang Niu

Gang Niu is currently an indefinite-term research scientist at RIKEN Center for Advanced Intelligence Project.

Xin Geng (Southeast University)
Masashi Sugiyama (RIKEN / The University of Tokyo)

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