Model-shared subspace boosting for multi-label classification

Typical approaches to 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 bootstrap 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 baseline classifie...

• Type: Conference
• Year: 2007
• Where: KDD
• Authors: Rong Yan, Jelena Tesic, John R. Smith
• Keywords: Data Mining, KDD 2007, Random Feature Subspace, Random Subspace Methods, Subspace Models