Self-Organizing Maps, Principal Components and Non-negative ...

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Self Organizing Maps Principal Components, Curves and Surfaces Non-negative Matrix Factorization Principal Curves Principal Components Principal Curves Spectral Clustering Generalization of the principal component line. First for random variables X ∈ R p . f (λ)... a parameterized smooth curve, a vector function with p coordinates Karoline Geissler Self-Organizing Maps, Principal Components and Non-negative M
Self Organizing Maps Principal Components, Curves and Surfaces Non-negative Matrix Factorization Principal Components Principal Curves Spectral Clustering For each data value x, let λf (x) define the closest point on the curve to x. The function f (λ) is called a principal curve for the distribution of X f (λ) = E(X | λf (X ) = λ) (9) f (λ) is the average of all data points that project to it. Karoline Geissler Self-Organizing Maps, Principal Components and Non-negative M
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