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Transductive De-Noising and Dimensionality Reduction using Total Bregman Regression
[chapter]
2006
Proceedings of the 2006 SIAM International Conference on Data Mining
Our goal on one hand is to use labels or other forms of ground truth data to guide the tasks of de-noising and dimensionality reduction and balance the objectives of better prediction and better data summarization, on the other hand it is to explicitly model the noise in the feature values. We use a generalization of L 2 loss, on which PCA and K-Means are based, to the Bregman family which, as a consequence widens the applicability of the proposed algorithms to cases where the data may be
doi:10.1137/1.9781611972764.51
dblp:conf/sdm/Acharyya06
fatcat:hwjkpqxmozdsvjtultizmnspfe