Kernel embedding - PowerPoint PPT Presentation

Return to Big Picture

Return to Big Picture. Main statistical goals of OODA: Understanding population structure Low dim ’ al Projections, PCA … Classification ( i . e. Discrimination) Understanding 2+ populations Time Series of Data Objects Chemical Spectra, Mortality Data

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Object Orie’d Data Analysis, Last Time

Object Orie’d Data Analysis, Last Time. Kernel Embedding Embed data in higher dimensional manifold Gives greater flexibility to linear methods Support Vector Machines Aimed at very non-Gaussian Data E.g. from Kernel Embedding Distance Weighted Discrimination HDLSS Improvement of SVM.

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889 views • 76 slides

Object Orie’d Data Analysis, Last Time

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784 views • 76 slides

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