17 citations · 32 across the 5 of their papers we have counts for
5 papers
Provable Subspace Identification Under Post-Nonlinear Mixtures
Qi Lyu, Xiao Fu
Unsupervised mixture learning (UML) aims at identifying linearly or nonlinearly mixed latent components in a blind manner. UML is known to be challenging: Even learning linear mixt…
On Finite-Sample Identifiability of Contrastive Learning-Based Nonlinear Independent Component Analysis
Qi Lyu, Xiao Fu
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-Guaranteed Simplex-Structured Post-Nonlinear Mixture Learning via Autoencoder
Qi Lyu, Xiao Fu
This work focuses on the problem of unraveling nonlinearly mixed latent components in an unsupervised manner. The latent components are assumed to reside in the probability simplex…
Understanding Latent Correlation-Based Multiview Learning and Self-Supervision: An Identifiability Perspective
Qi Lyu, Xiao Fu, Weiran Wang +1
Multiple views of data, both naturally acquired (e.g., image and audio) and artificially produced (e.g., via adding different noise to data samples), have proven useful in enhancin…
Nonlinear Multiview Analysis: Identifiability and Neural Network-assisted Implementation
Qi Lyu, Xiao Fu
Multiview analysis aims at extracting shared latent components from data samples that are acquired in different domains, e.g., image, text, and audio. Classic multiview analysis, e…