Self Organizing maps.ppt

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About Self organizing maps (SOM)

Transcript of Self Organizing maps.ppt

  • 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?