Localized Sliced Inverse Regression

Part of Advances in Neural Information Processing Systems 21 (NIPS 2008)

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Qiang Wu, Sayan Mukherjee, Feng Liang


We developed localized sliced inverse regression for supervised dimension reduction. It has the advantages of preventing degeneracy, increasing estimation accuracy, and automatic subclass discovery in classification problems. A semisupervised version is proposed for the use of unlabeled data. The utility is illustrated on simulated as well as real data sets.