Homework SourseExplain how SOM works based on your understandingSolutionA s
Solution
A self-organizing map or SOM is a kind of competitive network, which over time is tuned to represent the structure of a set of inputs.SOM are a data visualization technique which reduce the dimensions of data through the use of self-organizing neural networks.The problem that data visualization attempts to solve is that humans simply cannot visualize high dimensional data as is so techniques are created to help us understand this high dimensional data.
SOMs accomplish two things, they reduce dimensions and display similarities
Algorithm:
Initialize Map
For t from 0 to 1
Randomly select a sample
Get best matching unit
Scale neighbors
Increase t a small amount
End for
The first step in constructing a SOM is to initialize the weight vectors. From there you select a sample vector randomly and search the map of weight vectors to find which weight best represents that sample. Since each weight vector has a location, it also has neighboring weights that are close to it. The weight that is chosen is rewarded by being able to become more like that randomly selected sample vector. In addition to this reward, the neighbors of that weight are also rewarded by being able to become more like the chosen sample vector. From this step we increase t some small amount because the number of neighbors and how much each weight can learn decreases over time. This whole process is then repeated a large number of times.
In the case of colors, the program would first select a color from the array of samples such as green, then search the weights for the location containing the greenest color. From there, the colors surrounding that weight are then made more green. Then another color is chosen, such as red, and the process continues.
