Mostrar el registro sencillo de la publicación
A new fast training algorithm for autoencoder neural networks based on extreme learning machine
| dc.contributor.author | Vásquez-Coronel, José A | |
| dc.contributor.author | Mora, Marco | |
| dc.contributor.author | Vilches-Ponce, Karina | |
| dc.contributor.author | Silva Pavez, Fabián | |
| dc.contributor.author | Torres-Gonzalez, Italo | |
| dc.contributor.author | Barria-Valdevenito, Pedro | |
| dc.date.accessioned | 2023-03-08T13:28:10Z | |
| dc.date.available | 2023-03-08T13:28:10Z | |
| dc.date.issued | 2022 | |
| dc.identifier.uri | http://repositorio.ucm.cl/handle/ucm/4494 | |
| dc.description.abstract | Autoencoders are neural networks that are characterized by having the same inputs and outputs. This kind of Neural Networks aim to estimate a nonlinear transformation whose parameters allow to represent the input patterns to the network. The Extreme Learning Machine (ELM-AE) Autoencoders have random weights and biases in the hidden layer, and compute the output parameters by solving an overdetermined linear system using the Moore-Penrose Pseudoinverse. ELM-AE training is based on the Fast Iterative Shrinkage-Thresholding (FISTA). In this paper, we propose to improve the convergence speed obtained by FISTA considering the use of two algorithms of the Shrinkage-Thresholding class, namely Greedy FISTA and Linearly-Convergent FISTA. 6 frequently used public machine learning datasets were considered: MNIST, NORB, CIFAR10, UMist, Caltech256, Stanford Cars. Experiments were carried out varying the number of neurons in the hidden layer of the Autoencoders, considering the 3 algorithms, for all the databases. The experimental results showed that Greedy FISTA and Linearly-Convergent FISTA presented higher convergence speed, increasing the speed of ELM-Autoencoder training, maintaining a comparable generalization error between the three Shrinkage-Thresholding algorithms. | es_CL |
| dc.language.iso | en | es_CL |
| dc.rights | Atribución-NoComercial-SinDerivadas 3.0 Chile | * |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/cl/ | * |
| dc.source | International Conference on Automation/XXV Congress of the Chilean Association of Automatic Control (ICA-ACCA), Curicó, Chile, 1-7 | es_CL |
| dc.subject | Training | es_CL |
| dc.subject | Linear systems | es_CL |
| dc.subject | Machine learning algorithms | es_CL |
| dc.subject | Extreme learning machines | es_CL |
| dc.subject | Databases | es_CL |
| dc.subject | Neurons | es_CL |
| dc.subject | Machine learning | es_CL |
| dc.title | A new fast training algorithm for autoencoder neural networks based on extreme learning machine | es_CL |
| dc.type | Article | es_CL |
| dc.ucm.facultad | Facultad de Ciencias de la Ingeniería | es_CL |
| dc.ucm.indexacion | Scopus | es_CL |
| dc.ucm.uri | ieeexplore.ieee.org/document/10006276 | es_CL |
| dc.ucm.doi | doi.org/10.1109/ICA-ACCA56767.2022.10006276 | es_CL |
Ficheros en la publicación
| Ficheros | Tamaño | Formato | Ver |
|---|---|---|---|
|
No hay ficheros asociados a esta publicación. |
|||
Esta publicación aparece en la(s) siguiente(s) colección(ones)
Excepto si se señala otra cosa, la licencia de la publicación se describe como Atribución-NoComercial-SinDerivadas 3.0 Chile