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• W4296557273 abstract "The input data features set for many data driven tasks is high-dimensional while the intrinsic dimension of the data is low. Data analysis methods aim to uncover the underlying low dimensional structure imposed by the low dimensional hidden parameters by utilizing distance metrics that consider the set of attributes as a single monolithic set. However, the transformation of the low dimensional phenomena into the measured high dimensional observations might distort the distance metric, This distortion can effect the desired estimated low dimensional geometric structure. In this paper, we suggest to utilize the redundancy in the attribute domain by partitioning the attributes into multiple subsets we call views. The proposed methods utilize the agreement also called consensus between different views to extract valuable geometric information that unifies multiple views about the intrinsic relationships among several different observations. This unification enhances the information that a single view or a simple concatenations of views provides." @default.
• W4296557273 created "2022-09-21" @default.
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• W4296557273 date "2016-06-28" @default.
• W4296557273 modified "2023-09-24" @default.
• W4296557273 title "Multi-View Kernel Consensus For Data Analysis" @default.
• W4296557273 doi "https://doi.org/10.48550/arxiv.1606.08819" @default.
• W4296557273 hasPublicationYear "2016" @default.
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