The Self-Organizing Map (SOM) is a data visualization technique that can effectively reduce the dimensionality of high-dimensional data sets. SOMs are trained using an unsupervised learning algorithm, meaning they do not require any class labels or target values to learn the underlying structure of the data. Once the SOM has been trained, it gets used to map new data points onto the two-dimensional map in a way that preserves the topological relationships between the data points. This makes SOMs particularly well suited for exploratory data analysis, as they can provide a quick and intuitive way to visualize the structure of a high-dimensional data set.

SOMs are based on a neural network architecture, which means they are composed of interconnected nodes or neurons. Each neuron is connected to a set of input data points, and the strength of the connection between a neuron and an input data point is known as the weight of the connection. The weights of the connections between the neurons and the input data points are adjusted during the training process to minimize the error between the input data and the output of the SOM module. After the training process is complete, the SOM module will have learned to map the input data onto the two-dimensional map in a way that preserves the topological relationships between the data points.

The SOM Training Process

The SOM training process begins with an initialization step, in which the weights of the connections between the neurons and the input data points are initialized to random values. The SOM module is then trained using a set of input data points. For each data point in the training set, the neurons in the SOM are activated in a way that is determined by the weights of the connections between the neurons and the data point. The neuron with the most vital connection to the data point is known as the winning neuron, and the other neurons are said to be in its neighborhood.

The winning neuron and its neighborhood are then updated to match the input data point better. The update step is repeated for each data point in the training set, and the SOM converges to a state in which the weights of the connections between the neurons and the input data points have been optimized. After the training process is complete, the SOM can be used to map new data points onto the two-dimensional map.

The size of the neighborhood of the winning neuron is an essential parameter in the SOM training process. If the neighborhood is too large, the SOM module will not be able to learn the underlying structure of the data. If the neighborhood is too small, the SOM will be able to learn the underlying structure of the data, but it will be unable to generalize to new data points. The neighborhood size is typically chosen so that it decreases throughout the training process to allow the SOM to focus on the local structure of the data in the early stages of training and gradually learn the data’s global structure as training progresses.