Presentation on theme: "Coverage in Spring 2011: Transparencies for which it does not say cover will be skipped! COSC 6342: Support Vectors and using SVMs/Kernels for Regression,"— Presentation transcript:
Coverage in Spring 2011: Transparencies for which it does not say cover will be skipped! COSC 6342: Support Vectors and using SVMs/Kernels for Regression, PCA, and Outlier Detection Significantly edited and extended by Ch. Eick
7 Most α t are 0 and only a small number have α t >0; they are the support vectors Idea: If we remove all examples which are not support vectors from the dataset we still obtain the same hyperplane, but can do so more quickly!
COSC 6342: Using SVMs for Regression, PCA, and Outlier Detection
12 Example Flatness in Prediction For example, let us assume we predict the price of a house based on the number of rooms, and we have 2 functions: f1: #rooms* f2: #rooms* Both agree in their prediction for a two room house costing f1 is flatter than f2; f1 is less sensitive to noise Typically, flatness is measured using w which is for f2 and for f1; the lower w is the flatter f is… Consequently, w is minimized in support vector regression; however, in most cases, w 2 is minimized instead to get rid of the sqrt-function. Reminder: w sqrt(w w)=sqrt(w w T ) cover
Motivation Kernel PCA Example: we want to cluster the following dataset using K-means which will be difficult; idea: change coordinate system using a few new, non-linear features. 18 cover Remark: This approach uses kernels, but is unrelated to SVMs!
Kernel PCA Kernel PCA does PCA on the kernel matrix (equal to doing PCA in the mapped space selecting some orthogonal eigenvectors in the mapped space as the new coordinate system) Kind of PCA using non-linear transformations in the original space, moreover, the vectors of the chosen new coordinate system are usually not orthogonal in the original space. Then, ML/DM algorithms are used in the Reduced Feature Space. 19 cover Illustration: Original SpaceFeature Space features are a few linear combinations of features in the Feature Space PCA Reduced Feature Space (less dimensions)