Model-shared subspace boosting for multi-label classification

Typical approaches to the multi-label classification problem require learning an independent classifier for every label from all the examples and features. This can become a computational bottleneck for sizeable datasets with a large label space. In this paper, we propose an efficient and effective multi-label learning algorithm called model-shared subspace boosting (MSSBoost) as an attempt to reduce the information redundancy in the learning process. This algorithm automatically finds, shares and combines a number of base models across multiple labels, where each model is learned from random feature subspace and boots trap data samples. The decision functions for each label are jointly estimated and thus a small number of shared subspace models can support the entire label space. Our experimental results on both synthetic data and real multimedia collections have demonstrated that the proposed algorithm can achieve better classification performance than the non-ensemble baselineclassifiers with a significant speedup in the learning and prediction processes. It can also use a smaller number of base models to achieve the same classification performance as its non-model-shared counterpart.