A Fast Partial Label Learning Algorithm Based on Max-loss Function

In the age of big data, learning with weak supervision has become one of the hot research topics in machine learning field. Partial label learning, which deals with the problem where each training example is associated with a set of candidate labels among which only one label corresponds to the ground-truth, is an important weakly-supervised machine learning frameworks proposed recently and can be widely used in many real world tasks. The max-loss function may be used to accurately capture the relationship between the partial labeled sample and its labels. However, since the max-loss function usually brings us a nondifferentiable objective function difficult to be solved, it is rarely adopted in the existing algorithms. Moreover, the existing partial label learning algorithms can only deal with the problem with small-scale data, and rarely can be used to deal with big data. To cure above two problems, this paper presents a fast partial label learning algorithm with the max-loss function. The basic idea is to transform the nondifferentiable objective to a differentiable concave function by introducing the aggregate function to approximate the max(·) function involved in the max-lass function, and then to solve the obtained concave objective function by using a stochastic quasi-Newton method. The experimental results show that the proposed algorithm can not only achieve higher accuracy but also use shorter computing time than the state-of-the-art algorithms with average-loss functions. Moreover, the proposed algorithm can deal with the problems with millions samples within several minutes.