Large-Scale Structured Output Classification via Multiple Structured Support Vector Machine by Splitting
Chun‐Na Li, Yi Li, Yuan‐Hai Shao
Abstract
Structured support vector machine (SSVM) is an effective method on coping with problems involving complex outputs such as multiple dependent output variables and structured output spaces. However, its training process is very time consuming for large-scale data with complex structure and many classes. In this paper, to improve the efficiency of SSVM, we propose a multiple structured support vector machine (MSSVM) for structured output classification via the idea of splitting large into small. By constructing novel classification loss for each class, MSSVM solves a series of smaller optimization problems rather than one large-size optimization problem in SSVM. Therefore, MSSVM greatly reduces the training speed of SSVM. In addition, the structured output label information and discriminative information are embedded in the introduced losses in a simple but effective way. Experiments on multiclass classification, ordinal regression and hierarchical classification datasets demonstrate the efficiency and effectiveness of the proposed MSSVM.