Group Invariance-Based Twin Support Vector Machines(GI-TWSVM):The Problem and Its Consistency
Group invariance is a crucial type of prior knowledge often employed to enhance learning performance.As a binary classification support vector machine algorithm,twin support vector machines(TWSVM)can improve performance by exploring group invariance.Thus,in this paper,we propose to incorporate group invariance into the framework of TWSVM and thereby define the problem of group invariance-based twin support vector machines(GI-TWSVM)to improve the performance.First,an optimization problem is formulated for GI-TWSVM.Using the Twin Bounded Support Vector Machine(TBSVM)as an example,we develop two TBSVM algorithms that incorporate group invariance,demonstrating that the optimization problem is solvable and practically significant.Then,we systematically investigate the consistency of GI-TWSVM to build a solid theoretical basis for the related algorithms.Finally,experimental results using TBSVM as an example indicate that group invariance can significantly enhance the performance of twin support vector machine algorithms.
invariancegroup invariancetwin support vector machineconsistencyuniversal consistency
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