1. Feature Maps
• Geometric order in the brain
• sensory mapping
• Hubel and Wiesel - visual hypercolumns
• Kohonen's SOFM
• Hebbian learning with forgetting
• topological neighborhood
• algorithm description
• activity bubble formation
• relation to LBG
• Experimental results
2. Dimensionality Reduction
• Principle Components Analysis (PCA)
• Karhunen Loeve transform
• compression
• use in pattern recognition
• Oja's neural PCA
• Bottlenecking MLPs
• Sammon's nonlinear mapping (NLM)
• Exploratory projection pursuit (EPP)
3. Vector Quantization (VQ)
• Why VQ?
• underlying probability distribution approximation
• compression
• feature extraction
• Application to speech signals
• Kohonen's LVQ1

Assignment

1. Perform Kohonen's SOFM on the Iris or Peterson Barney data (whichever you used for the BP assignment).
   Compare error with LBG results.
2. Train a bottlenecking MLP using BP.
   Start with the 424 problem with all possible patterns as the training set.
   Train an 838 network with a random 50% subset of the patterns. How good is the generalization?