Deep Learning Generalized Structured Component Analysis
An Interpretable Artificial Neural Network Model with Composite Indexes
Bibliographic Data
| ID | 21641802 |
|---|---|
| Authors | Gyeongcheol Cho (0000-0002-9237-0388, McGill University), Heungsun Hwang (0000-0002-5057-7479, McGill University, corresponding author) |
| Year | 2024 |
| Volume | 31 |
| Issue | 2 |
| Pages | 265-279 |
| Publication date | 2024-03-03 |
| Peer Reviewed | Yes |
| Open Access | No |
| Type | ARTICLE |
| Venue | Structural Equation Modeling: A Multidisciplinary Journal (JOURNAL) |
| Journal identifiers | ISSN: 1070-5511 • E-ISSN: 1532-8007 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/10705511.2023.2234086 |
| OpenAlex | W4386156211 |
| Language | EN |
| Citations received | 2 |
| References cited | 49 |
Generalized structured component analysis (GSCA) is a multivariate method for specifying and examining interrelationships between observed variables and components. Despite its data-analytic flexibility honed over the decade, GSCA always defines every component as a linear function of observed variables, which can be less optimal when observed variables for a component are nonlinearly related, often reducing the component’s predictive power. To address this issue, we combine deep learning and GSCA into a single framework to allow a component to be a nonlinear function of observed variables without specifying the exact functional form in advance. This new method, termed deep learning generalized structured component analysis (DL-GSCA), aims to maximize the predictive power of components while their directed or undirected network remains interpretable. Our real and simulated data analyses show that DL-GSCA produces components with greater predictive power than those from GSCA in the presence of nonlinear associations between observed variables per component
Artificial neural network · Biology · Component analysis · Deep learning · Independent component analysis · Machine learning · Multivariate statistics · Predictive power · Statistics · Computer Science · Mathematics · Neural Networks and Applications · Remote-Sensing Image Classification · Spectroscopy and Chemometric Analyses · Artificial Intelligence
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| Unique citing works | 2 |
|---|---|
| Citations per year | 1 |
| Citation span | 2024 - 2026 (3) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 2 |