3ce3bd7d63a2c9c81983cc8e9bd02ae5-Supplemental.pdf

We start by restating the setup in which our algorithm operates. The type of ICA considered in our work assumes the following generative model. There are dsources recorded T times forming the columns of S:= [s1,...,sT] Rd T whose components s1t,...,sdt are assumed non-Gaussian and independent. Without loss of generality, we assume that each source has zero-mean, unit variance, and finite and distinct kurtosis, a common assumption among kurtosis-based ICA methods [12]. The kurtosis of a random variable v is defined as kurt[v] = E (v E(v))4 / E (v E(v))2 2. Finally, sources are assumed to be mixed through a linear system, i.e., there exists a full rank mixing matrix, A Rd d, producing the d-dimensional mixture, xt, expressed as xt = Ast t {1,...,T} .