Self-organizing MapsKevin Pang

GoalResearch SOMsCreate an introductory tutorial on the algorithmAdvantages / disadvantagesCurrent applicationsDemo program

Self-organizing MapsUnsupervised learning neural networkMaps multidimensional data onto a 2 dimensional gridGeometric relationships between image points indicate similarity

AlgorithmNeurons arranged in a 2 dimensional gridEach neuron contains a weight vectorExample: RGB values

Algorithm (continued)Initialize weightsRandomPregeneratedIterate through inputsFor each input, find the winning neuronEuclidean distanceAdjust winning neuron and its neighborsGaussianMexican hat

Optimization TechniquesReducing input / neuron dimensionalityRandom Projection methodPregenerating neuron weightsInitialize map closer to final stateRestricting winning neuron searchReduce the amount of exhaustive searches

ConclusionsAdvantagesData mapping is easily interpretedCapable of organizing large, complex data setsDisadvantagesDifficult to determine what input weights to useMapping can result in divided clustersRequires that nearby points behave similarly

Current ApplicationsWEBSOM: Organization of a Massive Document Collection

Current Applications (continued)Phonetic Typewriter

Current Applications (continued)Classifying World Poverty

Demo ProgramWritten for Windows with GLUT supportDemonstrates the SOM training algorithm in action

Demo Program DetailsRandomly initialized map100 x 100 grid of neurons, each containing a 3-dimensional weight vector representing its RGB valueTraining input randomly selected from 48 unique colorsGaussian neighborhood function

Screenshots

Questions?