Autoencoder Extreme Learning Machine for Fingerprint-Based Positioning: A Good Weight Initialization is Decisive

Autoencoder Extreme Learning Machine for Fingerprint-Based Positioning: A Good Weight Initialization is Decisive

Indoor positioning based on machine-learning (ML) models has attracted widespread interest in the last few years, given its high performance and usability. Supervised, semisupervised, and unsupervised models have thus been widely used in this field, not only to estimate the user position, but also t...

Full description

Saved in:
Bibliographic Details
Main Authors: Darwin P. Quezada Gaibor, Lucie Klus, Roman Klus, Elena Simona Lohan, Jari Nurmi, Mikko Valkama, Joaquin Huerta, Joaquin Torres-Sospedra
Format: Article
Language:English
Published: IEEE 2023-01-01
Series:IEEE Journal of Indoor and Seamless Positioning and Navigation
Subjects:
Autoencoder (AE)
extreme learning machine (ELM)
indoor positioning
singular value decomposition (SVD)
weight initialization
Wi-Fi fingerprinting
Online Access:https://ieeexplore.ieee.org/document/10195972/
Tags: Add Tag
No Tags, Be the first to tag this record!

Internet

https://ieeexplore.ieee.org/document/10195972/

Similar Items