Title
High Dimensional Clustering and Applications of Learning Methods: NonRedundant Clustering, Principal Feature Selection and Lear,Used
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This book is divided into two parts. The first part is about nonredundant clustering and feature selection for high dimensional data. The second part is on applying learning techniques to lung tumor imageguided radiotherapy. In the first part, a new clustering paradigm is investigated for exploratory data analysis: find all nonredundant clustering views of the data. Also a feature selection method is developed based on the popular transformation approach: principal component analysis (PCA). In the second part, machine learning algorithms are designed to aid lung tumor imageguided radiotherapy (IGRT). Specifically, intensive studies are preformed for gating and for directly tracking the tumor. For gating, two methods are developed: (1) an ensemble of templates where the representative templates are selected by Gaussian mixture clustering, and (2) a support vector machine (SVM) classifier with radial basis kernels. For the tracking problem, a multiple template matching method is explored to capture the varying tumor appearance throughout the different phases of the breathing cycle.
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