LP SVM with A Novel Similarity function Outperforms Powerful LP-QP-Kernel-SVM Considering Efficient Classification

Abstract

While the core quality of SVM comes from its ability to get the global optima, its classification performance also depends on computing kernels. However, while this kernel-complexity generates the power of machine, it is also responsible for the computational load to execute this kernel. Moreover, insisting on a similarity function to be a positive definite kernel demands some properties to be satisfied that seem unproductive sometimes raising a question about which similarity measures to be used for classifier. We model Vapnik’s LPSVM proposing a new similarity function replacing kernel function. Following the strategy of ”Accuracy first, speed second”, we have modelled a similarity function that is mathematically well-defined depending on analysis as well as geometry and complex enough to train the machine for generating solid generalization ability. Being consistent with the theory of learning by Balcan and Blum [1], our similarity function does not need to be a valid kernel function and demands less computational cost for executing compared to its counterpart like RBF or other kernels while provides sufficient power to the classifier using its optimal complexity. Benchmarking shows that our similarity function based LPSVM poses test error 0.86 times of the most powerful RBF based QP SVM but demands only 0.40 times of its computational cost.