This article considers high-order measures of independence for the independent component analysis problem and discusses the class of Jacobi algorithms for their optimization. Several implementations are discussed. We compare the proposed approaches with gradient-based techniques from the algorithmic point of view and also on a set of biomedical data.

Component states can be formalized in higher-order logic as (1) functions from variables to values and (2) records, among other possibilities. Variable-to-value maps are natural, but they yield weak typing and restrict the user to a predefined value space. Record types define component signatures and properties need to be transferred between the various signatures. The method yields strong typi...

2+1D multi-component $U(1)$ gauge theories with a Chern-Simons (CS) term provide simple and complete characterization of Abelian topological orders. In this paper, we extend the theory by taking number component fields to infinity find that they can describe interesting types 3+1D "fractonic" order. "Fractonic" describes peculiar phenomena point excitations in certain strongly interacting syste...

Given an n × 1 random vector X, independent component analysis (ICA) consists of finding a basis of Rn on which the coefficients of X are as independent as possible (in some appropriate sense). The change of basis can be represented by an n × n matrix B and the new coefficients given by the entries of vector Y = BX. When the observation vector X is modeled as a linear superposition of source si...