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

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F21%3APU142871" target="_blank" >RIV/00216305:26220/21:PU142871 - isvavai.cz</a>

Result on the web

<a href="https://www.researchgate.net/publication/340639579_Finding_the_optimal_number_of_low_dimension_with_locally_linear_embedding_algorithm" target="_blank" >https://www.researchgate.net/publication/340639579_Finding_the_optimal_number_of_low_dimension_with_locally_linear_embedding_algorithm</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3233/JCM-204198" target="_blank" >10.3233/JCM-204198</a>

Result language

angličtina

Original language name

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

Original language description

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.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

20202 - Communication engineering and systems

Project

<a href="/en/project/8JCH1067" target="_blank" >8JCH1067: Joint research of health diagnosis and advanced analysis based on ophthalmological medical data</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Publication year

2021

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Journal of Computational Methods in Sciences and Engineering

ISSN

1472-7978

e-ISSN

—

Volume of the periodical

20

Issue of the periodical within the volume

4

Country of publishing house

NL - THE KINGDOM OF THE NETHERLANDS

Number of pages

11

Pages from-to

1163-1173

UT code for WoS article

000611730000012

EID of the result in the Scopus database

2-s2.0-85100584673