Enhancing the Speed of the Learning Vector Quantization (LVQ) Algorithm by Adding Partial Distance Computation

Abstract Learning Vector Quantization (LVQ) is one of the most widely used classification approaches. LVQ faces a problem as when size data grows large it becomes slower. In this paper, modified version LVQ, which called PDLVQ proposed to accelerate traditional version. The scheme aims avoid unnecessary computations by applying an efficient Partial Distance (PD) computation strategy. Three different benchmark datasets are in experiments. comparisons have been done between and terms runtime result, turns out that shows better efficiency than LVQ. has achieved up 37% compared dimensions increased. Also, enhanced algorithm (PDLVQ) clear enhancement decrease dimensions, number clusters, or increased with