Multi-objective self-paced learning

Current self-paced learning (SPL) regimes adopt the greedy strategy to obtain the solution with a gradually increasing pace parameter while where to optimally terminate this increasing process is difficult to determine. Besides, most SPL implementations are very sensitive to initialization and short of a theoretical result to clarify where SPL converges to with pace parameter increasing. In this paper, we propose a novel multi-objective self-paced learning (MOSPL) method to address these issues. Specifically, we decompose the objective functions as two terms, including the loss and the self-paced regularizer, respectively, and treat the problem as the compromise between these two objectives. This naturally reformulates the SPL problem as a standard multi-objective issue. A multi-objective evolutionary algorithm is used to optimize the two objectives simultaneously to facilitate the rational selection of a proper pace parameter. The proposed technique is capable of ameliorating a set of solutions with respect to a range of pace parameters through finely compromising these solutions inbetween, and making them perform robustly even under bad initialization. A good solution can then be naturally achieved from these solutions by making use of some offthe- shelf tools in multi-objective optimization. Experimental results on matrix factorization and action recognition demonstrate the superiority of the proposed method against the existing issues in current SPL research.