We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Discussion of "Identifiability of latent-variable and structural-equation models: from linear to nonlinear".
- Authors
Matsuda, Takeru
- Abstract
This document discusses the identifiability theory for linear/nonlinear independent component analysis (ICA) models and structural equation models. It acknowledges Professor Hyvärinen for his contributions to machine learning and signal processing. The paper provides a comprehensive survey of the theory and application of nonlinear ICA and causal discovery, written in an accessible form for statisticians. It explores the identifiability of the nonlinear ICA model and discusses methods for solving it, such as exploiting temporal structure and learning via classification using neural networks. The text also mentions other statistical methods that utilize the idea of "learning via classification," such as bridge sampling and noise contrastive estimation. The author suggests that the exponential family interpretation can provide a theoretical basis for developing statistical methods that use pre-trained neural networks as feature extractors.
- Subjects
STRUCTURAL equation modeling; INDEPENDENT component analysis; EXPONENTIAL families (Statistics); SIGNAL processing; MACHINE learning; NONLINEAR theories
- Publication
Annals of the Institute of Statistical Mathematics, 2024, Vol 76, Issue 1, p39
- ISSN
0020-3157
- Publication type
Article
- DOI
10.1007/s10463-023-00885-3