On Finite-Sample Identifiability of Contrastive Learning-Based Nonlinear Independent Component Analysis

TL;DR

This paper provides the first finite-sample theoretical analysis of contrastive learning-based nonlinear ICA, addressing practical limitations of previous infinite-sample assumptions and offering insights into the trade-offs in function learner complexity.

Contribution

It introduces a finite-sample identifiability framework for GCL-based nICA, bridging the gap between theory and practice in unsupervised learning.

Findings

Finite-sample bounds for nICA identifiability

Trade-off between function complexity and approximation error

Validation of theoretical results through numerical experiments

Abstract

Nonlinear independent component analysis (nICA) aims at recovering statistically independent latent components that are mixed by unknown nonlinear functions. Central to nICA is the identifiability of the latent components, which had been elusive until very recently. Specifically, Hyv\"arinen et al. have shown that the nonlinearly mixed latent components are identifiable (up to often inconsequential ambiguities) under a generalized contrastive learning (GCL) formulation, given that the latent components are independent conditioned on a certain auxiliary variable. The GCL-based identifiability of nICA is elegant, and establishes interesting connections between nICA and popular unsupervised/self-supervised learning paradigms in representation learning, causal learning, and factor disentanglement. However, existing identifiability analyses of nICA all build upon an unlimited sample assumption and the use of ideal universal function learners -- which creates a non-negligible gap between theory and practice. Closing the gap is a nontrivial challenge, as there is a lack of established ``textbook'' routine for finite sample analysis of such unsupervised problems. This work puts forth a finite-sample identifiability analysis of GCL-based nICA. Our analytical framework judiciously combines the properties of the GCL loss function, statistical generalization analysis, and numerical differentiation. Our framework also takes the learning function's approximation error into consideration, and reveals an intuitive trade-off between the complexity and expressiveness of the employed function learner. Numerical experiments are used to validate the theorems.