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Enhancing Both Efficiency and Representational Capability of Isomap by Extensive Landmark Selection
Mathematical Problems in Engineering
The problems of improving computational efficiency and extending representational capability are the two hottest topics in approaches of global manifold learning. In this paper, a new method called extensive landmark Isomap (EL-Isomap) is presented, addressing both topics simultaneously. On one hand, originated from landmark Isomap (L-Isomap), which is known for its high computational efficiency property, EL-Isomap also possesses high computational efficiency through utilizing a small set ofdoi:10.1155/2015/241436 fatcat:qklwb7ybbrhyxng64rcotzzj2i