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Neural Processing Letters

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

  • 27-06-2022
Published in:
Authors
Tanzeel U. Rehman
Mahwish Yousaf
Li Jing
Published in
Neural Processing Letters | Issue 9/2023

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Abstract

This article introduces A*-FastIsomap, a method that leverages the A* Search Algorithm to improve the performance of Classical Isomap. Classical Isomap, a global Nonlinear Dimensionality Reduction (NLDR) method, faces computational challenges when calculating the Shortest Path Distance (SPD) for high-dimensional data. A*-FastIsomap addresses these issues by using the A* search algorithm with Double Buckets (ASBD) instead of the Dijkstra algorithm, significantly reducing computational time and improving accuracy. The method was evaluated on five high-dimensional datasets, demonstrating its effectiveness and efficiency compared to classical methods. The article also discusses related work in NLDR and provides a detailed comparison of computational time complexities of various methods.
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previous article A Comprehensive Survey on Various Fully Automatic Machine Translation Evaluation Metrics
Title
A*-FastIsomap: An Improved Performance of Classical Isomap Based on A* Search Algorithm
Authors
Tanzeel U. Rehman
Mahwish Yousaf
Li Jing
Publication date
27-06-2022
Publisher
Springer US
Published in
Neural Processing Letters / Issue 9/2023
Print ISSN: 1370-4621
Electronic ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-022-10941-3
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