Convergence to the Wasserstein Distance

Oct 13, 2025 by miodeya.com 2 views

Introduction

The Earth Mover's Distance (EMD) is a measure of similarity between two probability distributions. It is a fundamental concept in computer science, statistics, and machine learning, with applications in data clustering, image processing, and natural language processing. In this article, we will delve into the complexity of computing the EMD, exploring its theoretical foundations, computational algorithms, and asymptotic behavior.

Setup

Given two samples of size $n$ , denoted as $\text{Sample}_1=(x_1,\dots,x_n)$ and $\text{Sample}_2=(y_1,\dots,y_n)$ , where each datapoint has feature dimension $d$ , we aim to compute the EMD between these two samples. The EMD is defined as the minimum cost of transforming one distribution into another, where the cost is measured by the amount of "work" required to move one unit of mass from one distribution to another.

Theoretical Foundations

The EMD is a special case of the more general concept of optimal transport, which was first introduced by Monge in 1781. The EMD can be formulated as a linear programming problem, where the goal is to find the optimal transport plan that minimizes the total cost of moving one unit of mass from one distribution to another.

Computational Algorithms

There are several algorithms available for computing the EMD, including:

Linear Programming Relaxation (LPR): This algorithm solves the linear programming relaxation of the EMD problem, which is a convex optimization problem. The LPR algorithm has a time complexity of $O(n^3)$ , where $n$ is the size of the input samples.
Sinkhorn Algorithm: This algorithm is a variant of the LPR algorithm that uses a iterative approach to solve the EMD problem. The Sinkhorn algorithm has a time complexity of $O(n^2)$ , making it faster than the LPR algorithm for large input samples.
EMD Algorithm: This algorithm is a direct implementation of the EMD problem, which has a time complexity of $O(n^3)$ .

Asymptotic Behavior

The EMD has several interesting asymptotic properties, including:

Convergence to the Wasserstein Distance: As the size of the input samples increases, the EMD converges to the Wasserstein distance, which is a measure of similarity between two probability distributions.
Sub-Gaussian Convergence Rate: The EMD has a sub-Gaussian convergence rate, which means that the EMD converges to the true value at a rate that is faster than the Gaussian distribution.

Computational Complexity

The computational complexity of computing the EMD depends on the algorithm used and the size of the input samples. In general, the EMD has a time complexity of $O(n^3)$ , where $n$ is the size of the input samples. However, the Sinkhorn algorithm has a time complexity of $O(n^2)$ , making it faster than the LPR algorithm for large input samples.

Comparison with Other Similarity Measures

The EMD has several advantages over other similarity measures, including:

Robustness to Outliers: The EMD is robust to outliers, which means that it is less sensitive to noisy or anomalous data points.
Ability to Handle High-Dimensional Data: The EMD can handle high-dimensional data, which makes it suitable for applications in image processing and natural language processing.

Conclusion

In conclusion, the EMD is a powerful similarity measure that has several advantages over other similarity measures. Its computational complexity depends on the algorithm used and the size of the input samples, but it has a time complexity of $O(n^3)$ in general. The EMD has several interesting asymptotic properties, including convergence to the Wasserstein distance and a sub-Gaussian convergence rate. Its robustness to outliers and ability to handle high-dimensional data make it a suitable choice for applications in image processing and natural language processing.

Future Work

There are several directions for future research on the EMD, including:

Development of Faster Algorithms: Developing faster algorithms for computing the EMD is an active area of research.
Application to Real-World Problems: Applying the EMD to real-world problems is an exciting area of research, with potential applications in image processing, natural language processing, and data clustering.

References

Monge, G. (1781): "Mémoire sur la théorie des déblais et des remblais."
Rubner, Y., Tomasi, C., & Guibas, L. J. (1998): "The Earth Mover's Distance as a Metric for Image Retrieval."
Cuturi, M. (2013): "Sinkhorn Distances: Lights on the Edge."
Peyré, G., & Cuturi, M. (2019): "Computational Optimal Transport."
Q&A: Earth Mover's Distance =============================

Q: What is the Earth Mover's Distance (EMD)?

A: The Earth Mover's Distance (EMD) is a measure of similarity between two probability distributions. It is a fundamental concept in computer science, statistics, and machine learning, with applications in data clustering, image processing, and natural language processing.

Q: How is the EMD defined?

A: The EMD is defined as the minimum cost of transforming one distribution into another, where the cost is measured by the amount of "work" required to move one unit of mass from one distribution to another.

Q: What is the relationship between the EMD and the Wasserstein Distance?

A: The EMD converges to the Wasserstein distance as the size of the input samples increases. The Wasserstein distance is a measure of similarity between two probability distributions.

Q: What are the advantages of the EMD over other similarity measures?

A: The EMD has several advantages over other similarity measures, including:

Robustness to Outliers: The EMD is robust to outliers, which means that it is less sensitive to noisy or anomalous data points.
Ability to Handle High-Dimensional Data: The EMD can handle high-dimensional data, which makes it suitable for applications in image processing and natural language processing.

Q: What are the computational complexities of the EMD algorithms?

A: The computational complexities of the EMD algorithms depend on the algorithm used and the size of the input samples. In general, the EMD has a time complexity of $O(n^3)$ , where $n$ is the size of the input samples. However, the Sinkhorn algorithm has a time complexity of $O(n^2)$ , making it faster than the LPR algorithm for large input samples.

Q: Can the EMD be used for image processing and natural language processing?

A: Yes, the EMD can be used for image processing and natural language processing. Its ability to handle high-dimensional data and robustness to outliers make it a suitable choice for these applications.

Q: What are the applications of the EMD in data clustering?

A: The EMD has several applications in data clustering, including:

Clustering High-Dimensional Data: The EMD can be used to cluster high-dimensional data, which is a challenging task due to the curse of dimensionality.
Robust Clustering: The EMD is robust to outliers, which makes it suitable for clustering noisy or anomalous data.

Q: Can the EMD be used for anomaly detection?

A: Yes, the EMD can be used for anomaly detection. Its ability to handle high-dimensional data and robustness to outliers make it a suitable choice for detecting anomalies in data.

Q: What are the future directions for research on the EMD?

A: There are several directions for future research on the EMD, including:

Development of Faster Algorithms: Developing faster algorithms for computing the EMD is an active area of research.
Application to Real-World Problems: Applying the EMD to real-world problems is an exciting area of research, with potential applications in image processing, natural language processing, and data clustering.

Q: What are the references for further reading on the EMD?

A: Some references for further reading on the EMD include:

Monge, G. (1781): "Mémoire sur la théorie des déblais et des remblais."
Rubner, Y., Tomasi, C., & Guibas, L. J. (1998): "The Earth Mover's Distance as a Metric for Image Retrieval."
Cuturi, M. (2013): "Sinkhorn Distances: Lights on the Edge."
Peyré, G., & Cuturi, M. (2019): "Computational Optimal Transport."