A*-FastIsomap: An Improved Performance of Classical Isomap Based on A* Search Algorithm

Tanzeel U. Rehman, Mahwish Yousaf, Jing Li

Abstract: Nonlinear Dimensionality Reduction (NLDR) is a well-known approach of manifold learning to transform the data from high to low dimensional space. After studying various techniques proposed for the NLDR, we find that performance improvement is still required. Therefore, we adopt classical Isomap, which reduces Shortest Path Distance (SPD) and high computational time cost problems. These problems are occurring due to the Dijkstra algorithm. This paper presents the A*-FastIsomap method for SPD issues, which is based on the A* Search Algorithm with the Double Buckets algorithm. We compared the A*-FastIsomap with classical Isomap to verify its better efficiency and results for high dimensional datasets with much higher accuracy. The outcome of our current study demonstrates that as compared to classical Isomap, our proposed A*-FastIsomap is faster and more accurate. Furthermore, our proposed method can reduce the computation time for high and large-dimensional datasets.