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Classical dependence measures such as Pearson correlation, Spearman's ρ, and Kendall's
τ can detect only monotonic or linear dependence. To overcome these limitations, Székely et …

Most data is multi-dimensional. Discovering whether any subset of dimensions, or
subspaces, shows dependence is a core task in data mining. To do so, we require a …

We introduce a mixture of probabilistic canonical correlation analyzers model for analyzing
local correlations, or more generally mutual statistical dependencies, in cooccurring data …

Pearson product-moment correlation coefficients are a well-practiced quantification of linear
dependence seen across many fields. When calculating a sample-based correlation …

Supplementary Material for A General Framework for Association Analysis of
Heterogeneous Data. We provide proofs, technical details of the algorithm, a detailed …

We introduce the dependence distance, a new notion of the intrinsic distance between
points, derived as a pointwise extension of statistical dependence measures between …

We present ennemi, a Python package for correlation analysis based on mutual information
(MI). MI is a measure of relationship between variables. Unlike Pearson correlation it is valid …

The maximum information coefficient (MIC) is a novel and widely-using measure of
association detection in large datasets. The most outstanding feature of MIC is that it has …

In recent years, the advances in data collection and statistical analysis promotes canonical
correlation analysis (CCA) available for more advanced research. CCA is the main …