Self-organizing Maps Kevin Pang

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Self-organizing Maps Kevin Pang

Goal Research SOMs Create an introductory tutorial on the algorithm Advantages / disadvantages Current applications Demo program

Self-organizing Maps Unsupervised learning neural network Maps multidimensional data onto a 2 dimensional grid Geometric relationships between image points indicate similarity

Algorithm Neurons arranged in a 2 dimensional grid Each neuron contains a weight vector Example: RGB values

Algorithm (continued ) Initialize weights Random Pregenerated Iterate through inputs For each input, find the “winning” neuron Euclidean distance Adjust “winning” neuron and its neighbors Gaussian Mexican hat

Optimization Techniques Reducing input / neuron dimensionality Pregenerating neuron weights Random Projection method Initialize map closer to final state Restricting “winning” neuron search Reduce the amount of exhaustive searches

Conclusions Advantages Data mapping is easily interpreted Capable of organizing large, complex data sets Disadvantages Difficult to determine what input weights to use Mapping can result in divided clusters Requires that nearby points behave similarly

Current Applications WEBSOM: Organization of a Massive Document Collection

Current Applications (continued) Phonetic Typewriter

Current Applications (continued) Classifying World Poverty

Demo Program Written for Windows with GLUT support Demonstrates the SOM training algorithm in action

Demo Program Details Randomly initialized map 100 x 100 grid of neurons, each containing a 3-dimensional weight vector representing its RGB value Training input randomly selected from 48 unique colors Gaussian neighborhood function



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