Looking at models in high-dimensional data spaces
Seminar Room 1, Newton Institute
What do the fishing net models of self-organizing maps look like in the data space? How do the estimated mean vectors and variance-covariance ellipses from model-based clustering fit to the clusters? How does small n, large p affect the variability in the esimates of the separating hyperplane from support vector machine models? These are a few of the things that we may discuss in this talk. The goal is to calibrate participants' eyes to viewing high-dimensional spaces and stimulate thought about what types of plots might accompany high-dimensional statistical analysis
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