AERed: An Autoencoder–Decoder Dimensionality Reduction Method for Wearable-Based Human Activity Recognition-Reference-Cited by-同舟云学术

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AERed: An Autoencoder–Decoder Dimensionality Reduction Method for Wearable-Based Human Activity Recognition

Published:2023-12-01 Issue:23 Volume:23 Page:29804-29814
ISSN:1530-437X
Container-title:IEEE Sensors Journal
language:
Short-container-title:IEEE Sensors J.

Author:

Anjali ¹^ORCID,Dai Hong-Ning²^ORCID,Kumar Jitendra³,Pandey Om Jee⁴^ORCID

Affiliation:

1. Department of Information Technology, ABV-IIITM, Gwalior, India

2. Department of Computer Science, Hong Kong Baptist University, Kowloon Tong, Hong Kong

3. Department of Computer Applications, National Institute of Technology Tiruchirappalli, Tiruchirappalli, India

4. Department of Electronics Engineering, Indian Institute of Technology (BHU) Varanasi, Varanasi, India

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Instrumentation

Link

http://xplorestaging.ieee.org/ielx7/7361/10336249/10295396.pdf?arnumber=10295396

Reference54 articles.

1. Global versus local methods in nonlinear dimensionality reduction;de silva;Proc Adv Neural Inf Process Syst,2003

2. Training Design, Performance Analysis, and Talent Identification—A Systematic Review about the Most Relevant Variables through the Principal Component Analysis in Soccer, Basketball, and Rugby

3. Bearing Fault Diagnosis Based on Statistical Locally Linear Embedding

4. Dimensionality reduction in the wild: Gaps and guidance;sedlmair,2012

5. Diversity-aware classifier ensemble selection via f-score

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