Nonlinear Independent Component Analysis

Nonlinear Independent Component Analysis (nICA) aims to recover independent latent sources from their nonlinearly mixed observations, a challenging problem due to inherent non-identifiability. Current research focuses on establishing identifiability conditions through constraints on the mixing function (e.g., orthogonality of Jacobian columns) or by leveraging auxiliary data (e.g., temporal structure, labels) to achieve conditional independence among latent variables. These advancements are significant for unsupervised representation learning, enabling principled disentanglement of factors in high-dimensional data and improving the efficiency of personalized decision-making in applications like healthcare.