Radial Basis Functions (RBFs) are a class of functions used in various areas of machine learning and computational mathematics. They are primarily employed in the context of interpolation, where the goal is to find a smooth surface that fits a set of points. RBFs have also been widely used in the field of neural networks, where they serve as activation functions in Radial Basis Function Networks (RBFNs).

What is a Radial Basis Function?

A radial basis function is a real-valued function whose value depends only on the distance from a central point. More formally, an RBF is a function $$\phi: \mathbb{R}^n \rightarrow \mathbb{R}$$ such that $$\phi(\mathbf{x}) = \phi(\|\mathbf{x} - \mathbf{c}\|)$$, where $$\|\cdot\|$$ typically denotes the Euclidean distance, although other distance metrics can also be used, and $$\mathbf{c}$$ is the center of the function.

The "radial" aspect refers to the circular symmetry around the center point $$\mathbf{c}$$ in the domain of the function. That is, the function's value at a point only depends on the radial distance from the center, not the direction.

There are several types of RBFs, each with different characteristics. The most commonly used RBFs include:

• Gaussian: $$\phi(r) = e^{-(\varepsilon r)^2}$$, where $$\varepsilon$$ is a shape parameter.
• Multiquadric: $$\phi(r) = \sqrt{1 + (\varepsilon r)^2}$$.
• Inverse Multiquadric: $$\phi(r) = \frac{1}{\sqrt{1 + (\varepsilon r)^2}}$$.
• Linear: $$\phi(r) = r$$.
• Cubic: $$\phi(r) = r^3$$.
• Thin Plate Spline: $$\phi(r) = r^2 \log(r)$$, which is particularly popular in 2D interpolation problems.

The choice of RBF depends on the nature of the problem being solved, and different RBFs have different properties that make them suitable for various applications.

RBF interpolation is a method for constructing an interpolant function from a set of scattered data points in multidimensional space. The interpolant is a linear combination of radial basis functions, each associated with a point in the dataset:

$$s(\mathbf{x}) = \sum_{i=1}^{N} \lambda_i \phi(\|\mathbf{x} - \mathbf{x}_i\|)$$,

where $$s$$ is the interpolant, $$\mathbf{x}_i$$ are the data points, $$\lambda_i$$ are the coefficients to be determined, and $$\phi$$ is the chosen RBF.

The coefficients $$\lambda_i$$ are typically found by solving a system of linear equations that arises from enforcing the interpolant to match the given data values at the data points.

In the context of neural networks, an RBFN is a type of artificial neural network that uses radial basis functions as activation functions. The output of the network is a linear combination of radial basis functions of the inputs and neuron parameters, which are adjusted during training.

RBFNs are often used for function approximation, classification, and time-series prediction. They have the advantage of being able to approximate any continuous function with arbitrary accuracy, given enough hidden units and properly selected parameters.

• Flexibility: RBFs can approximate any smooth function given enough RBF centers.
• Local Relevance: Changes in one part of the RBF network do not affect the entire network, which is beneficial for learning local features.
• Simple Training: Training an RBF network is often faster and simpler than training other types of neural networks.

However, there are also disadvantages to consider:

• Curse of Dimensionality: The number of RBF centers can grow exponentially with the dimensionality of the input space, leading to computational difficulties.
• Center Placement: The performance of RBF methods is highly dependent on the placement of the centers, which can be nontrivial to determine.
• Overfitting: Without proper regularization, RBF networks can overfit the training data, especially if there are too many RBF centers.

Conclusion

Radial Basis Functions are powerful tools in the machine learning toolbox. They provide a method for constructing flexible models that can capture complex relationships in data. RBFs are particularly well-suited for problems where the underlying function is believed to be smooth or where local interactions are predominant. As with any machine learning technique, careful consideration must be given to the choice of RBF, the placement of centers, and the prevention of overfitting to ensure the best performance.