spectralbrain.compute_gps#
- spectralbrain.compute_gps(decomp, *, skip_zero=True)[source]#
Global Point Signature — spectral embedding of the surface.
Embeds each point into a high-dimensional space where Euclidean distance equals diffusion distance (at t → ∞).
\[\text{GPS}(x) = \left( \frac{\varphi_1(x)}{\sqrt{\lambda_1}},\; \frac{\varphi_2(x)}{\sqrt{\lambda_2}},\; \ldots,\; \frac{\varphi_k(x)}{\sqrt{\lambda_k}} \right)\]Warning
GPS is not sign/ordering invariant. Eigenvectors have arbitrary sign (φ and −φ are both valid), so direct comparison between subjects requires sign alignment. For group-level analysis, prefer HKS or WKS which use φ² and are sign-invariant.
- Parameters:
decomp (SpectralDecomposition)
skip_zero (bool) – Exclude the constant eigenfunction (λ₀ ≈ 0).
- Returns:
ndarray, shape (N, d) – Spectral embedding. d = k−1 if skip_zero, else d = k.
- Return type:
References
Rustamov RM. Laplace–Beltrami eigenfunctions for deformation invariant shape representation. SGP 2007.