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Mathematics in Independent Component Analysis

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92 Chapter 4. Neurocomput<strong>in</strong>g 64:223-234, 2005<br />

identifiability issues, albeit explicitly only <strong>in</strong> the two-dimensional analytic case.<br />

Extend<strong>in</strong>g his ideas, we are able to f<strong>in</strong>d a new necessary condition — which<br />

we name ’absolutely degenerate’, see def<strong>in</strong>ition 6 — for identify<strong>in</strong>g mix<strong>in</strong>g<br />

structure us<strong>in</strong>g only the boundary. This together with the generalization to<br />

arbitrary dimensions is our ma<strong>in</strong> contribution here, stated <strong>in</strong> theorem 7.<br />

The paper is arranged as follows: Section 2 presents a simple result about<br />

homogeneous functions and shortly discusses l<strong>in</strong>ear identifiability <strong>in</strong> the case<br />

of bounded random variables. Section 3 states the postnonl<strong>in</strong>ear separability<br />

problem, which is then proved <strong>in</strong> the follow<strong>in</strong>g section for real-valued random<br />

vectors. In section 5, a simulation confirm<strong>in</strong>g the ma<strong>in</strong> separability theorem<br />

is presented.<br />

Postnonl<strong>in</strong>ear separability is important for any postnonl<strong>in</strong>ear ICA algorithm,<br />

so we focus only on this question. We do not propose an explicit postnonl<strong>in</strong>ear<br />

identification algorithm but <strong>in</strong>stead refer to [9–13] for both algorithms and<br />

simulations.<br />

2 Basics<br />

For n ∈ N let Gl(n) be the general l<strong>in</strong>ear group of R n i.e. group of <strong>in</strong>vertible<br />

real (n × n)−matrices. An <strong>in</strong>vertible matrix L ∈ Gl(n) is said to be a scal<strong>in</strong>g<br />

matrix, if it is diagonal. We say two (m × n)−matrices B, C are equivalent,<br />

B ∼ C, if C can be written as C = BPL with an scal<strong>in</strong>g matrix L ∈ Gl(n)<br />

and an <strong>in</strong>vertible matrix with unit vectors <strong>in</strong> each row (permutation matrix)<br />

P ∈ Gl(n).<br />

Def<strong>in</strong>ition 1 Given a function f : U → R assume there exist a, b ∈ R such<br />

that at least one is not of absolute value 0 or 1. If f(ax) = bf(x) for all x ∈ U<br />

with ax ∈ U, then f is said to be (a, b)-homogeneous or simply homogeneous.<br />

The follow<strong>in</strong>g lemma characteriz<strong>in</strong>g homogeneous functions is from [10]. However<br />

we added the correction to exclude the cases |a| or |b| ∈ {0, 1}, because<br />

<strong>in</strong> these cases homogeneity does not <strong>in</strong>duce such strong results. This lemma<br />

can be generalized to cont<strong>in</strong>uously differentiable functions, so the strong assumption<br />

of analyticity is not needed, but shortens the proof.<br />

Lemma 2 [10] Let f : U → R, be an analytic function that is (a, b)homogeneous<br />

on [0, ε) with ε > 0. Then there exist c ∈ R, n ∈ N ∪ {0}<br />

(possibly 0) such that f(x) = cx n for all x ∈ U.<br />

PROOF. If |a| is <strong>in</strong> {0, 1} or b = 0 then obviously f ≡ 0. If b = −1 then<br />

f ≡ 0, s<strong>in</strong>ce |a| /∈ {0, 1}, f(a 2 x) = f(x) and f cont<strong>in</strong>uous, f is constant, but<br />

3

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