This paper applies statistical dependence measures to interpret self-supervised learning (SSL). Conventional applications of measures like mutual information commonly use separate procedures for feature extraction and dependence estimation, where the relationship between optimal features and the strength of dependence is unclear. This causes limitations in tasks requiring multivariate feature representations, particularly in SSL. The recently introduced multivariate measure, functional maximal correlation, is a unified framework based on orthonormal decomposition of density ratios, wherein the spectrum and the bases become the measure and the features, respectively. This paper proposes that features in SSL can also be interpreted as basis functions of the density ratio. We introduce the Hierarchical Functional Maximal Correlation Algorithm (HFMCA), a theoretically justified approach that ensures faster convergence, enhanced stability, and prevents feature collapse by learning orthonormal bases as multivariate features.