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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20202 - Communication engineering and systems

Projekt

<a href="/cs/project/8JCH1067" target="_blank" >8JCH1067: Společný výzkum zdravotní diagnostiky a pokročilé analýzy oftalmologických medicínských dat</a><br>

Návaznosti

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

Rok uplatnění

2021

Kód důvěrnosti údajů

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

Název periodika

Journal of Computational Methods in Sciences and Engineering

ISSN

1472-7978

e-ISSN

—

Svazek periodika

20

Číslo periodika v rámci svazku

4

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

11

Strana od-do

1163-1173

Kód UT WoS článku

000611730000012

EID výsledku v databázi Scopus

2-s2.0-85100584673