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- W1550614472 abstract "In many domains there will exist different representations or “views” describing the same set of objects. Taken alone, these views will often be deficient or incomplete. Therefore a key problem for exploratory data analysis is the integration of multiple views to discover the underlying structures in a domain. This problem is made more difficult when disagreement exists between views. We introduce a new unsupervised algorithm for combining information from related views, using a late integration strategy. Combination is performed by applying an approach based on matrix factorization to group related clusters produced on individual views. This yields a projection of the original clusters in the form of a new set of “meta-clusters” covering the entire domain. We also provide a novel model selection strategy for identifying the correct number of meta-clusters. Evaluations performed on a number of multi-view text clustering problems demonstrate the effectiveness of the algorithm." @default.
- W1550614472 created "2016-06-24" @default.
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- W1550614472 date "2009-01-01" @default.
- W1550614472 modified "2023-10-16" @default.
- W1550614472 title "A Matrix Factorization Approach for Integrating Multiple Data Views" @default.
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- W1550614472 doi "https://doi.org/10.1007/978-3-642-04180-8_45" @default.
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