Algorithms from Invariants: Smoothed Analysis of Orbit Recovery over $SO(3)$
This work addresses a fundamental challenge in computational biology and signal processing by providing efficient algorithms for 3D structure recovery, though it represents an incremental advance over prior theoretical bounds.
The paper tackles the problem of orbit recovery over SO(3), which involves reconstructing a function on a sphere from noisy, rotated copies, with applications in molecular structure recovery via cryo-electron tomography. It introduces a quasi-polynomial time algorithm based on smoothed analysis and frequency marching, achieving provable sample complexity bounds polynomial in the signal dimension.
In this work we study orbit recovery over $SO(3)$, where the goal is to recover a function on the sphere from noisy, randomly rotated copies of it. We assume that the function is a linear combination of low-degree spherical harmonics. This is a natural abstraction for the problem of recovering the three-dimensional structure of a molecule through cryo-electron tomography. For provably learning the parameters of a generative model, the method of moments is the standard workhorse of theoretical machine learning. It turns out that there is a natural incarnation of the method of moments for orbit recovery based on invariant theory. Bandeira et al. [BBSK+18] used invariant theory to give tight bounds on the sample complexity in terms of the noise level. However many of the key challenges remain: Can we prove bounds on the sample complexity that are polynomial in $n$, the dimension of the signal? The bounds in [BBSK+18] hide constants that have an unspecified dependence on $n$ and only hold in the limit as $σ^2 \rightarrow \infty$ where $σ^2$ is the variance of the noise. Moreover can we give efficient algorithms? We revisit these challenges from the perspective of smoothed analysis, where we assume that the coefficients of the signal, in the basis of spherical harmonics, are subject to small random perturbations. Our main result is a quasi-polynomial time algorithm for orbit recovery over $SO(3)$ in this model. Our approach is based on frequency marching, which sequentially solves linear systems to find higher degree coefficients. Our main technical contribution is to show that these linear systems have unique solutions, are well-conditioned, and that the error can be made to compound over at most a logarithmic number of rounds. We believe that our work takes an important first step towards uncovering the algorithmic implications of invariant theory.