spectralbrain.compute_si_hks#

spectralbrain.compute_si_hks(decomp, *, n_times=256, n_frequencies=8)[source]#

Scale-Invariant HKS — removes scale dependence from HKS.

Under uniform scaling β the HKS undergoes a log-time shift and amplitude change. SI-HKS eliminates both via:

  1. Sample HKS at log-spaced times τ = log(t).

  2. Take derivative w.r.t. τ (removes amplitude).

  3. Apply DFT; keep modulus of first n_frequencies coefficients (removes shift).

\[\text{SI-HKS}(x) = \left| \mathcal{F}\left\{ \frac{\partial}{\partial \tau} \text{HKS}(x, e^\tau) \right\} \right|_{1:n}\]
Parameters:

  • decomp (SpectralDecomposition)

  • n_times (int) – Number of log-time samples (FFT input length). Power of 2 recommended.

  • n_frequencies (int) – Number of Fourier modulus coefficients to keep.

Returns:

ndarray, shape (N, n_frequencies) – Scale-invariant spectral descriptor.

Return type:

ndarray[tuple[Any, …], dtype[floating]]

References

Bronstein MM, Kokkinos I. Scale-invariant heat kernel signatures for non-rigid shape recognition. CVPR 2010.