Self-Organizing Maps for Dimension Reduction, Data Visualization, and Clustering. In this post, I share my understanding of SOM, how it learns, methods, and limitations of the SOM.
Self-Organizing Map (SOM) is one of the common unsupervised neural network models. SOM has been widely used for clustering, dimension reduction, and feature detection. SOM was first introduced by Professor Kohonen. For this reason, SOM also called Kohonen Map. It has many real-world applications including machine state monitoring, fault identification, satellite remote sensing process, and robot control . Moreover, SOM is used for data visualization by projecting higher dimensional data into lower dimensional space leveraging topological similarity properties. In the process of model development, SOM utilizes competitive learning techniques while the conventional neural network applies an error reduction process through backpropagation with a gradient descent algorithm. In this post, I share my understanding of SOM, how it learns, methods, and limitations of the SOM.
How does SOM learn?
The architecture for SOM is very similar to the Neural network architecture but it is much simpler than the artificial neural network (ANN). SOM one contains two layers: an input layer and an output layer (feature map). SOM starts feature mapping by initializing a weight vector. Weights in SOM have a completely different connotation than in ANN. In modeling a neural network: activation function is applied to the linear combination of the weights and input values to produce output for each of the neurons in the architecture. On the other hand, SOM does not apply the activation function. and employ weights as the characteristics of the neuron in the architecture. The weights are generally randomly generated. The motivation of using the weight vector as neuron characteristics is to push each of the row (observations) of the given data into an imaginary space where each of the rows acts as a point. This is the core of the SOM. Once, each of the data holds an imaginary point into the input space, then a search for closest points (data).
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