measuring Statistical Dependence with Hilbert-Schmidt Norm

"Hilbert-Schmist Norm of the Cross-Covariance operator" is proposed as an independence criterion in reproducing kernel Hilbert spaces (RKHSs). The measure is refereed to as Hilbert-Schmidt Independence Criterion, or HSIC. An empirical estimate of this measure is introduced which may be well used in many practical application such as independent Component Analysis (ICA), Maximum Variance Unfolding (MVU), feature extraction, feature selection, ... .

RKHS Theory

Let [math]\displaystyle{ \mathcal{F} }[/math] be a Hilbert space from [math]\displaystyle{ \mathcal{X} }[/math] to [math]\displaystyle{ \mathbb{R} }[/math]. We assume [math]\displaystyle{ \mathcal{F} }[/math] is a Reproducing Kernel Hilbert Space,i.e., for all [math]\displaystyle{ x\in \mathcal{X} }[/math], the corresponding Dirac evaluation operator [math]\displaystyle{ \delta_x:\mathcal{F} \rightarrow \mathbb{R} }[/math] is a bounded (or equivalently continuous) linear functional. We denote the kernel of this operator by [math]\displaystyle{ k(x,x')=\langle \phi(x)\phi(x') \rangle_{\mathcal{F}} }[/math] where [math]\displaystyle{ k:\mathcal{X}\times \mathcal{X}\rightarrow \mathbb{R} }[/math] is a positive definite function and [math]\displaystyle{ \phi }[/math] is the feature map of [math]\displaystyle{ \mathcal{F} }[/math]. Similarly, we consider another RKHS named [math]\displaystyle{ \mathcal{G} }[/math] with Domain [math]\displaystyle{ \mathcal{Y} }[/math], kernel [math]\displaystyle{ l(\cdot,\cdot) }[/math] and feature map [math]\displaystyle{ \psi }[/math]. We assume both [math]\displaystyle{ \mathcal{F} }[/math] and [math]\displaystyle{ \mathcal{G} }[/math] are separable, i.e., they have a complete orthogonal basis.