Detail publikace

Finding the optimal number of low dimension with locally linear embedding algorithm

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"
}