Definition (G(R): Dimensionless normalized second moment of R)
Assume R is a k-dimensional polytope with finite volume V(R) with centroid at origin yminR∫∥x−y∥2dx=R∫∥x∥2dxThe dimensionless normalized second moment of R (a basic cell) is G(R)≜k1V(R)1+2/k1R∫∥x∥2dx
Proposition
Let α>0 and R⊂Rk, let αR={αx:x∈R}. Our quantity G(R) is scale-invariant; i.e. ∀α>0 G(αR)=G(R)
Intuition
This is a scale-invariant quantity that depends on the shape of our cell R and characterizes how good our Lattice Vector Quantizer is.
Definition (High-resolution conditions for LVQ)
Assuming High-Resolution Conditions (i.e. X∼f where f smooth and basic cell R0 small enough) we have that the LVQ Distortion can be approximated as D(QΛ)≈V(R0)1R0∫∥x∥2dxand using the dimensionless second moment of R0 we can redefine it as D(QΛ)≈V(R0)2/kG(R0)
Note
H(R0)≜V(R0)1R0∫∥x∥2dxis the moment of inertia of the k-dimensional convex polytope R0.
Proposition
Under high-resolution conditions the dimensionless normalized second moment of R0 (i.e. G(R0)) is the appropriate measure for comparing LVQs.
Definition (Minimum normalized second moment)
We define the minimum normalized second moment for k-dimensional lattices as Gk=Λ⊂RkminG(R0)
Proposition
The minimum normalized second moment, Gk is lower bounded by the normalized second moment of a sphere Gk≥G(Sk)where Sk={x∈Rk:∥x∥≤1}.