Probabilistic Curve Learning: Coulomb Repulsion and the Electrostatic Gaussian Process (1506.03768v1)

Abstract: Learning of low dimensional structure in multidimensional data is a canonical problem in machine learning. One common approach is to suppose that the observed data are close to a lower-dimensional smooth manifold. There are a rich variety of manifold learning methods available, which allow mapping of data points to the manifold. However, there is a clear lack of probabilistic methods that allow learning of the manifold along with the generative distribution of the observed data. The best attempt is the Gaussian process latent variable model (GP-LVM), but identifiability issues lead to poor performance. We solve these issues by proposing a novel Coulomb repulsive process (Corp) for locations of points on the manifold, inspired by physical models of electrostatic interactions among particles. Combining this process with a GP prior for the mapping function yields a novel electrostatic GP (electroGP) process. Focusing on the simple case of a one-dimensional manifold, we develop efficient inference algorithms, and illustrate substantially improved performance in a variety of experiments including filling in missing frames in video.

• The paper's main contribution is the introduction of the Coulomb Repulsive Process, which mitigates GP-LVM identifiability issues by modeling data points as repelling particles.
• It presents the Electrostatic Gaussian Process that fuses a Gaussian process mapping with electrostatic repulsion to robustly learn low-dimensional manifolds.
• Experiments demonstrate enhanced performance in tasks like filling missing video frames, underscoring the method’s efficacy in practical manifold inference.

The paper "Probabilistic Curve Learning: Coulomb Repulsion and the Electrostatic Gaussian Process" addresses the challenge of learning low-dimensional structures within multidimensional data, a central problem in machine learning. This paper particularly focuses on improving the modeling of smooth manifolds which the observed data points are assumed to be near. The existing methods for manifold learning map data points effectively to the manifold but fall short in probabilistically learning both the manifold and the associated generative distribution of the observed data.

A standard approach has been the Gaussian Process Latent Variable Model (GP-LVM), but its performance is hindered by identifiability issues. To mitigate these issues, the authors propose a novel approach called the Coulomb Repulsive Process (Corp), inspired by principles from physics, specifically electrostatic interactions.

Key Contributions:

1. Coulomb Repulsive Process (Corp): The core idea is to treat the locations of points on the manifold as particles that repel each other according to Coulomb's law, akin to electrostatic repulsion. This repulsion helps in better separating and identifying the points on a lower-dimensional manifold, thereby addressing the identifiability issues seen in GP-LVM.
2. Electrostatic Gaussian Process (ElectroGP): By combining the Coulomb Repulsive Process with a Gaussian Process (GP) prior for the mapping function, the authors introduce a new process termed as ElectroGP. This combination capitalizes on the strengths of GPs while incorporating the spatial repulsion mechanism.
3. Inference and Efficiency: The focus is primarily on the case of a one-dimensional manifold because it simplifies the development and evaluation of efficient inference algorithms. Through these algorithms, the authors demonstrate improved performance over existing methods in various experiments.

Applications and Experimental Results:

The efficacy of the proposed method is demonstrated through experiments that involve practical applications such as filling in missing frames in video sequences. The results indicate that the ElectroGP method substantially outperforms existing approaches, providing more accurate and robust estimations of the missing data. This suggests the potential of ElectroGP in handling tasks where the data has an intrinsic low-dimensional structure that needs to be probabilistically modeled along with the generative processes involved.

In summary, this paper presents a significant advancement in probabilistic curve learning by addressing the shortcomings of the GP-LVM with a novel approach inspired by physical models. The introduction of the Coulomb Repulsive Process and the Electrostatic Gaussian Process represents a notable contribution to the field, offering promising improvements in practical applications involving multidimensional data.