A Bayesian Approach Toward Robust Multidimensional Ellipsoid-Specific Fitting

This work presents a novel and effective method for fitting multidimensional ellipsoids (
i.e.
, ellipsoids embedded in
$\mathbb {R}^{n}$
) to scattered data in the contamination of noise and outliers. Unlike conventional algebraic or geometric fitting paradigms that assume each measurement point is a noisy version of its nearest point on the ellipsoid, we approach the problem as a
Bayesian parameter estimate process
and maximize the posterior probability of a certain ellipsoidal solution given the data. We establish a more robust correlation between these points based on the
predictive distribution
within the Bayesian framework,
i.e.
, considering each model point as a potential source for generating each measurement. Concretely, we incorporate a uniform prior distribution to constrain the search for primitive parameters within an ellipsoidal domain, ensuring
ellipsoid-specific
results regardless of inputs. We then establish the connection between measurement point and model data via Bayes' rule to enhance the method's robustness against noise. Due to independent of spatial dimensions, the proposed method not only delivers high-quality fittings to challenging elongated ellipsoids but also generalizes well to multidimensional spaces. To address outlier disturbances, often overlooked by previous approaches, we further introduce a uniform distribution on top of the
predictive distribution
to significantly enhance the algorithm's robustness against outliers. Thanks to the uniform prior, our maximum a posterior probability coincides with a more tractable maximum likelihood estimation problem, which is subsequently solved by a numerically stable
Expectation Maximization
(EM) framework. Moreover, we introduce an
$\varepsilon$-accelerated technique
to expedite the convergence of EM considerably. We also investigate the relationship between our algorithm and conventional least-squares-based ones, during which we theoretically prove our method's superior robustness. To the best of our knowledge, this is the first comprehensive method capable of performing multidimensional ellipsoid-specific fitting within the Bayesian optimization paradigm under diverse disturbances. We evaluate it across lower and higher dimensional spaces in the presence of heavy noise, outliers, and substantial variations in axis ratios. Also, we apply it to a wide range of practical applications such as
microscopy cell counting
,
3D reconstruction
,
geometric shape approximation
, and
magnetometer calibration
tasks. In all these test contexts, our method consistently delivers flexible, robust, ellipsoid-specific performance, and achieves the state-of-the-art results.