A Bayesian Gaussian Process-Based Latent Discriminative Generative Decoder (LDGD) Model for High-Dimensional Data-Reference-Cited by-同舟云学术

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A Bayesian Gaussian Process-Based Latent Discriminative Generative Decoder (LDGD) Model for High-Dimensional Data

Published:2024 Issue: Volume:12 Page:113314-113335
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Ziaei Navid¹^ORCID,Nazari Behzad¹^ORCID,Eden Uri T.²,Widge Alik S.³^ORCID,Yousefi Ali⁴

Affiliation:

1. Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, Iran

2. Department of Mathematics and Statistics, Boston University, Boston, MA, USA

3. Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA

4. Department of Biomedical Engineering, University of Houston, Houston, TX, USA

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/6287639/10380310/10636145.pdf?arnumber=10636145

Reference55 articles.

1. A Comprehensive Review of Dimensionality Reduction Techniques for Feature Selection and Feature Extraction

2. Dimensionality reduction of calcium-imaged neuronal population activity

3. Principal component analysis based methods in bioinformatics studies

4. Using PCA and factor analysis for dimensionality reduction of bio-informatics data;Ali;arXiv:1707.07189,2017

5. Forecasting daily stock market return using dimensionality reduction

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