Robust Metrics for Comparing One-Dimensional Functions: Stability to Noise, Perturbations, and Changes in Projection Angle

Core Concepts

The Volterra p-norms and distances exhibit stability properties that make them robust to a broad class of perturbations, including changes in projection angle and additive noise, when comparing one-dimensional functions.

Abstract

The paper studies a family of distances between functions of a single variable, called the Volterra p-norms and distances. These distances are examples of integral probability metrics, which have been used previously for comparing probability measures.
The key insights and findings are:

Robustness to 1D perturbations: The Volterra distances are robust to integral-preserving changes of variable. The distance between a function and its perturbation is bounded by a measure of the size of the perturbation.

Robustness to changes in projection angle: The Volterra distance between two one-dimensional projections of a two-dimensional function is bounded by the size of the difference in projection angles.

Robustness to 2D perturbations: The Volterra distance between two one-dimensional projections of a two-dimensional function and its perturbation is bounded by the size of the perturbation.

Convergence of the discrete norm: For a broad class of functions, the discrete Volterra norm of samples converges to the continuous Volterra norm as the number of samples increases.

Robustness to noise: The discrete Volterra norm of a noisy, sampled function converges to the norm of the noiseless function as the number of samples increases, while the discrete norm of the noise vector vanishes.

These stability properties make the Volterra distances attractive for applications such as clustering tomographic projection images, where robustness to perturbations and noise is crucial.

Stats

The Volterra p-norm of a function f on [a, b] is defined as:
∥f∥V p = ∥Vf∥Lp
where (Vf)(x) = ∫_a^x f(t) dt.
The discrete Volterra p-norm of a vector f of samples of f on an equispaced grid is defined as:
∥f∥νp = ∥Vf∥τp
where V is the discrete Volterra operator and ∥·∥τp is the trapezoidal rule approximation to the Lp norm.

Quotes

"The Volterra distances exhibit the behavior described by the bound in Theorem 4.1: the distances grow as concave functions of the dilation size."
"The Volterra 1 and 2 distances grow monotonically with θ throughout the entire range of values, where the Volterra ∞ distance and the Lebesgue distances plateau when θ is big enough so that the numerical supports of the projected bumps are disjoint."

Key Insights Distilled From

On metrics robust to noise and deformations

by William Leeb at arxiv.org 05-07-2024

https://arxiv.org/pdf/2101.10867.pdf

Deeper Inquiries

How can the insights from this work on one-dimensional functions be extended to higher dimensional settings, such as for comparing 3D volumes in cryo-electron microscopy?

In the context of comparing 3D volumes in cryo-electron microscopy, the insights gained from the study on one-dimensional functions and the robustness properties of Volterra distances can be extended in several ways.

Extension to Multi-Dimensional Functions: The principles and techniques used for comparing one-dimensional functions can be extended to multi-dimensional functions representing 3D volumes. By considering projections along different axes or planes, similar distance metrics can be defined to compare the features and structures of 3D volumes.

Robustness to Deformations and Noise: Just as the study demonstrated the robustness of Volterra distances to perturbations and noise in one-dimensional functions, similar robustness can be applied to 3D volumes. This can help in comparing volumes that may have undergone deformations or contain noise, ensuring that only meaningful differences are captured.

Handling Heterogeneity: In cryo-electron microscopy, datasets often contain multiple conformations or variations within the same molecule. The robustness properties of Volterra distances can be utilized to compare volumes with heterogeneity, allowing for meaningful comparisons while accounting for variations.

Incorporating Rotation and Translation: Similar to the analysis of projection angles in the study, the comparison of rotated or translated 3D volumes can benefit from the insights on how distances change with different transformations. This can aid in aligning and comparing volumes from different orientations.

Error Bounds and Approximations: The error bounds and convergence properties discussed in the study can be extended to 3D volumes, providing a framework for quantifying errors in approximations and ensuring convergence of distance metrics as the resolution or sampling density increases.

By extending the principles of robustness, stability, and error analysis from one-dimensional functions to higher dimensional settings like 3D volumes in cryo-electron microscopy, researchers can develop reliable and effective methods for comparing complex biological structures with confidence.

How can the insights from this work on one-dimensional functions be extended to higher dimensional settings, such as for comparing 3D volumes in cryo-electron microscopy?

The insights from this work on one-dimensional functions can be extended to higher dimensional settings, such as comparing 3D volumes in cryo-electron microscopy, in several ways:

Extension to Multi-Dimensional Metrics: The principles of robustness and stability observed in the study can be applied to develop distance metrics for comparing 3D volumes. By considering projections, rotations, and translations in higher dimensions, similar robustness properties can be achieved.

Handling Deformations and Noise: Just as the study demonstrated the robustness of Volterra distances to perturbations and noise in one-dimensional functions, these properties can be leveraged to compare 3D volumes that may have undergone deformations or contain noise, ensuring reliable comparisons.

Accounting for Heterogeneity: In cryo-electron microscopy, datasets often contain heterogeneous structures or multiple conformations. The insights on robustness to perturbations can help in comparing volumes with variations, allowing for meaningful analysis while mitigating the impact of heterogeneity.

Incorporating Rotation and Translation: Similar to the analysis of projection angles in the study, the comparison of rotated or translated 3D volumes can benefit from understanding how distances change with different transformations. This can aid in aligning and comparing volumes from different orientations.

Error Analysis and Approximations: The error bounds and convergence properties discussed in the study can be extended to 3D volumes, providing a framework for quantifying errors in approximations and ensuring convergence of distance metrics as the complexity of the data increases.

By extending the insights from one-dimensional functions to higher dimensional settings like 3D volumes in cryo-electron microscopy, researchers can develop robust and reliable methods for comparing complex biological structures effectively.

How can the insights from this work on one-dimensional functions be extended to higher dimensional settings, such as for comparing 3D volumes in cryo-electron microscopy?

The insights from this work on one-dimensional functions can be extended to higher dimensional settings, such as comparing 3D volumes in cryo-electron microscopy, in the following ways:

Multi-Dimensional Metrics: The principles of robustness and stability observed in the study can be extended to develop distance metrics for comparing 3D volumes. By considering projections, rotations, and translations in higher dimensions, similar robustness properties can be achieved.

Deformations and Noise: The robustness of Volterra distances to perturbations and noise in one-dimensional functions can be applied to 3D volumes. This ensures reliable comparisons even in the presence of deformations or noise in the data.

Heterogeneity Handling: The insights on robustness to perturbations can help in comparing 3D volumes with heterogeneity, such as different conformations of molecules in cryo-electron microscopy datasets. This allows for meaningful comparisons while accounting for variations.

Rotation and Translation: Similar to the analysis of projection angles in the study, understanding how distances change with rotations and translations can aid in comparing 3D volumes from different orientations. This can help align and analyze volumes effectively.

Error Bounds and Convergence: The error bounds and convergence properties discussed in the study can be extended to 3D volumes, providing a framework for quantifying errors in approximations and ensuring convergence of distance metrics as the complexity of the data increases.

By extending the insights from one-dimensional functions to higher dimensional settings like 3D volumes in cryo-electron microscopy, researchers can develop robust and reliable methods for comparing complex biological structures effectively.