Please use this identifier to cite or link to this item:
https://hdl.handle.net/10356/159584
Title: | Manifold learning based on straight-like geodesics and local coordinates | Authors: | Ma, Zhengming Zhan, Zengrong Feng, Zijian Guo, Jiajing |
Keywords: | Engineering::Computer science and engineering | Issue Date: | 2020 | Source: | Ma, Z., Zhan, Z., Feng, Z. & Guo, J. (2020). Manifold learning based on straight-like geodesics and local coordinates. IEEE Transactions On Neural Networks and Learning Systems, 32(11), 4956-4970. https://dx.doi.org/10.1109/TNNLS.2020.3026426 | Journal: | IEEE Transactions on Neural Networks and Learning Systems | Abstract: | In this article, a manifold learning algorithm based on straight-like geodesics and local coordinates is proposed, called SGLC-ML for short. The contribution and innovation of SGLC-ML lie in that; first, SGLC-ML divides the manifold data into a number of straight-like geodesics, instead of a number of local areas like many manifold learning algorithms do. Figuratively speaking, SGLC-ML covers manifold data set with a sparse net woven with threads (straight-like geodesics), while other manifold learning algorithms with a tight roof made of titles (local areas). Second, SGLC-ML maps all straight-like geodesics into straight lines of a low-dimensional Euclidean space. All these straight lines start from the same point and extend along the same coordinate axis. These straight lines are exactly the local coordinates of straight-like geodesics as described in the mathematical definition of the manifold. With the help of local coordinates, dimensionality reduction can be divided into two relatively simple processes: calculation and alignment of local coordinates. However, many manifold learning algorithms seem to ignore the advantages of local coordinates. The experimental results between SGLC-ML and other state-of-the-art algorithms are presented to verify the good performance of SGLC-ML. | URI: | https://hdl.handle.net/10356/159584 | ISSN: | 2162-237X | DOI: | 10.1109/TNNLS.2020.3026426 | Schools: | Interdisciplinary Graduate School (IGS) | Rights: | © 2020 IEEE. All rights reserved. | Fulltext Permission: | none | Fulltext Availability: | No Fulltext |
Appears in Collections: | IGS Journal Articles |
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