An Improved Reduced Set Method to Control the Run-time Complexity of SVM in Wireless Sensor Networks

One prominent disadvantage of SVM when implemented in wireless sensor networks (WSNs) is the run-time complexity of classifier, which linearly increases with the number of support vectors (SVs). This disadvantage prevents applying SVM in some applications. In this paper, we propose an improved reduced set method to find solutions characterized by few number of vectors and having good generalization properties. The idea behind our improved method is to combine finding patterns with maximum absolute margin and performing gradient-descent to find new patterns in new decision function. Our method can partially overcome the non-convexity difficulty. The application context is that of WSNs, where a general sensor node is equipped with fixed point CPU. The performance of fixed point implementation of our algorithm is also provided.