Sign-Aware Multistate Jaccard Kernels and Geometry for Real and Complex-Valued Signals
This work addresses the need for interpretable similarity measures in scientific and financial applications, offering a novel extension of Jaccard/Tanimoto methods beyond nonnegative data.
The authors tackled the problem of extending overlap-based distances to real- and complex-valued signals while preserving metric and kernel properties, resulting in a framework that provides bounded distances, positive-semidefinite kernels, probabilistic semantics, and budget accounting in a unified approach.
We introduce a sign-aware, multistate Jaccard/Tanimoto framework that extends overlap-based distances from nonnegative vectors and measures to arbitrary real- and complex-valued signals while retaining bounded metric and positive-semidefinite kernel structure. Formally, the construction is a set- and measure-theoretic geometry: signals are represented as atomic measures on a signed state space, and similarity is given by a generalized Jaccard overlap of these measures. Each signal is embedded into a nonnegative multistate representation, using positive/negative splits for real signals, Cartesian and polar decompositions for complex signals, and user-defined state partitions for refined regime analysis. Applying the Tanimoto construction to these embeddings yields a family of $[0,1]$ distances that satisfy the triangle inequality and define positive-semidefinite kernels usable directly in kernel methods and graph-based learning. Beyond pairwise distances, we develop coalition analysis via Möbius inversion, which decomposes signal magnitude into nonnegative, additive contributions with exact budget closure across coalitions of signals. Normalizing the same embeddings produces probability measures on coordinate -- state configurations, so that the distance becomes a monotone transform of total variation and admits a regime -- intensity decomposition. The resulting construction yields a single, mechanistically interpretable distance that simultaneously provides bounded metric structure, positive-semidefinite kernels, probabilistic semantics, and transparent budget accounting within one sign-aware framework, supporting correlograms, feature engineering, similarity graphs, and other analytical tools in scientific and financial applications.