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.
In numerous areas REF there will exist distinctive representations or "perspectives" portraying the same arrangement of items.
A Matrix Factorization Approach for Integrating Multiple Data Views
ECML/PKDD
Mathematics Computer Science