Using state space differential geometry for nonlinear blind source separation

TL;DR

This paper introduces a differential geometry-based method for nonlinear blind source separation that identifies independent sources by analyzing the metric structure of stimulus state space, reducing the problem to linear BSS when possible.

Contribution

It proposes a novel geometric approach to nonlinear BSS using local velocity correlations as a metric, enabling intrinsic source separation without iterative procedures.

Findings

Successfully separates sources in nonlinear BSS scenarios.

Reduces nonlinear BSS to linear BSS when data are separable.

Provides a geometric framework applicable to various stimulus evolutions.

Abstract

Given a time series of multicomponent measurements of an evolving stimulus, nonlinear blind source separation (BSS) seeks to find a "source" time series, comprised of statistically independent combinations of the measured components. In this paper, we seek a source time series with local velocity cross correlations that vanish everywhere in stimulus state space. However, in an earlier paper the local velocity correlation matrix was shown to constitute a metric on state space. Therefore, nonlinear BSS maps onto a problem of differential geometry: given the metric observed in the measurement coordinate system, find another coordinate system in which the metric is diagonal everywhere. We show how to determine if the observed data are separable in this way, and, if they are, we show how to construct the required transformation to the source coordinate system, which is essentially unique except for an unknown rotation that can be found by applying the methods of linear BSS. Thus, the proposed technique solves nonlinear BSS in many situations or, at least, reduces it to linear BSS, without the use of probabilistic, parametric, or iterative procedures. This paper also describes a generalization of this methodology that performs nonlinear independent subspace separation. In every case, the resulting decomposition of the observed data is an intrinsic property of the stimulus' evolution in the sense that it does not depend on the way the observer chooses to view it (e.g., the choice of the observing machine's sensors). In other words, the decomposition is a property of the evolution of the "real" stimulus that is "out there" broadcasting energy to the observer. The technique is illustrated with analytic and numerical examples.