Learning From Data Lecture 19 A Peek At Unsupervised Learning k-Means Clustering Probability Density Estimation Gaussian Mixture Models M. Magdon-Ismail CSCI 4100/6100 recap: Radial Basis Functions Nonparametric RBF Parametric k-RBF-Network g(x)= N n=1 α n (x) ∑ N m=1 α m (x) · y n α n (x)= φ || x−xn || r (bump on x) r =0.05 No Training h(x)= w 0 + k j=1 w j · φ || x − μ j || r = w t Φ(x) (bump on μj ) linear model given μ j choose μ j as centers of k-clusters of data x y x y k =4,r = 1 k k = 10, regularized c AM L Creator: Malik Magdon-Ismail Unsupervised Learning:2 /23 Unsupervised learning −→ Unsupervised Learning • Preprocessor to organize the data for supervised learning: Organize data for faster nearest neighbor search Determine centers for RBF bumps. • Important to be able to organize the data to identify patterns. Learn the patterns in data, e.g. the patterns in a language before getting into a supervised setting. amazon.com organizes books into categories c AM L Creator: Malik Magdon-Ismail Unsupervised Learning:3 /23 Clustering digits −→ Clustering Digits 21-NN rule, 10 Classes 10 Clustering of Data 0 1 2 3 4 6 7 8 9 Average Intensity Symmetry c AM L Creator: Malik Magdon-Ismail Unsupervised Learning:4 /23 Clustering −→
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A Peek At Unsupervised Learning Lecture 19 Learning From ...magdon/courses/LFD-Slides/SlidesLect19-C.pdf · Clustering A cluster is a collection of points S A k-clustering is a partition
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• Preprocessor to organize the data for supervised learning:Organize data for faster nearest neighbor search
Determine centers for RBF bumps.
• Important to be able to organize the data to identify patterns.Learn the patterns in data, e.g. the patterns in a language before getting into a supervised setting.