Přístupnostní navigace
E-přihláška
Vyhledávání Vyhledat Zavřít
Detail publikace
YANG, T. FU, D. MENG, J PAN, J BURGET, R.
Originální název
Finding the optimal number of low dimension with locally linear embedding algorithm
Typ
článek v časopise ve Web of Science, Jimp
Jazyk
angličtina
Originální abstrakt
1) The problem this paper is going to solve is how to determine the optimal number of dimension when using dimensionality reduction methods, and in this paper, we mainly use local linear embedding (LLE) method as example. 2) The solution proposed is on the condition of the parameter k in LLE is set in advance. Firstly, we select the parameter k, and compute the distance matrix of each feature in the source data and in the data after dimensionality reduction. Then, we use the Log-Euclidean metric to compute the divergence of the distance matrix between the features in the original data and in the low-dimensional data. Finally, the optimal low dimension is determined by the minimum Log-Euclidean metric. 3) The performances are verified by a public dataset and a handwritten digit dataset experiments and the results show that the dimension found by the method is better than other dimension number when classifying the dataset.
Klíčová slova
Manifold learning; LLE; Log-Euclidean metric; distance matrix
Autoři
YANG, T.; FU, D.; MENG, J; PAN, J; BURGET, R.
Vydáno
19. 1. 2021
Nakladatel
IOS PRESS
Místo
AMSTERDAM
ISSN
1472-7978
Periodikum
Journal of Computational Methods in Sciences and Engineering
Ročník
20
Číslo
4
Stát
Nizozemsko
Strany od
1163
Strany do
1173
Strany počet
11
URL
https://www.researchgate.net/publication/340639579_Finding_the_optimal_number_of_low_dimension_with_locally_linear_embedding_algorithm
BibTex
@article{BUT175739, author="YANG, T. and FU, D. and MENG, J and PAN, J and BURGET, R.", title="Finding the optimal number of low dimension with locally linear embedding algorithm", journal="Journal of Computational Methods in Sciences and Engineering", year="2021", volume="20", number="4", pages="1163--1173", doi="10.3233/JCM-204198", issn="1472-7978", url="https://www.researchgate.net/publication/340639579_Finding_the_optimal_number_of_low_dimension_with_locally_linear_embedding_algorithm" }