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

SCOPUSTM   
Citations 50

1
Updated on Sep 15, 2023

Web of ScienceTM
Citations 50

1
Updated on Sep 17, 2023

Page view(s)

38
Updated on Sep 19, 2023

Google ScholarTM

Check

Altmetric


Plumx

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.