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- W2003202408 abstract "We present a randomized algorithm for semi-supervised learning of Mahalanobis metrics over Rn. The inputs to the algorithm are a set, U , of unlabeled points in Rn, a set of pairs of points, S = {(x, y)i};x, y ∈ U , that are known to be similar, and a set of pairs of points, D = {(x, y)i};x, y ∈ U , that are known to be dissimilar. The algorithm randomly samples S, D, and m-dimensional subspaces of Rn and learns a metric for each subspace. The metric over Rn is a linear combination of the subspace metrics. The randomization addresses issues of efficiency and overfitting. Extensions of the algorithm to learning non-linear metrics via kernels, and as a pre-processing step for dimensionality reduction are discussed. The new method is demonstrated on a regression problem (structure-based chemical shift prediction) and a classification problem (predicting clinical outcomes for immunomodulatory strategies for treating severe sepsis)." @default.
- W2003202408 created "2016-06-24" @default.
- W2003202408 creator A5042353780 @default.
- W2003202408 date "2005-12-01" @default.
- W2003202408 modified "2023-09-23" @default.
- W2003202408 title "A RANDOMIZED ALGORITHM FOR LEARNING MAHALANOBIS METRICS: APPLICATION TO CLASSIFICATION AND REGRESSION OF BIOLOGICAL DATA" @default.
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- W2003202408 doi "https://doi.org/10.1142/9781860947292_0025" @default.
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