Heterogeneous data fusion via space alignment using nonmetric multidimensional scaling

Jaegul Choo, Shawn Bohn, Grant C. Nakamura, Amanda M. White, Haesun Park

Research output: Chapter in Book/Report/Conference proceedingConference contribution

17 Citations (Scopus)

Abstract

Heterogeneous data sets are typically represented in different feature spaces, making it difficult to analyze relationships spanning different data sets even when they are semantically related. Data fusion via space alignment can remedy this task by integrating multiple data sets lying in different spaces into one common space. Given a set of reference correspondence data that share the same semantic meaning across different spaces, space alignment attempts to place the corresponding reference data as close together as possible, and accordingly, the entire data are aligned in a common space. Space alignment involves optimizing two potentially conflicting criteria: minimum deformation of the original relationships and maximum alignment between the different spaces. To solve this problem, we provide a novel graph embedding framework for space alignment, which converts each data set into a graph and assigns zero distance between reference correspondence pairs resulting in a single graph. We propose a graph embedding method for fusion based on nonmetric multidimensional scaling (MDS). Its criteria using the rank order rather than the distance allows nonmetric MDS to effectively handle both deformation and alignment. Experiments using parallel data sets demonstrate that our approach works well in comparison to existing methods such as constrained Laplacian eigenmaps, Procrustes analysis, and tensor decomposition. We also present standard cross-domain information retrieval tests as well as interesting visualization examples using space alignment.

Original languageEnglish
Title of host publicationProceedings of the 12th SIAM International Conference on Data Mining, SDM 2012
PublisherSociety for Industrial and Applied Mathematics Publications
Pages177-188
Number of pages12
ISBN (Print)9781611972320
DOIs
Publication statusPublished - 2012
Event12th SIAM International Conference on Data Mining, SDM 2012 - Anaheim, CA, United States
Duration: 2012 Apr 262012 Apr 28

Publication series

NameProceedings of the 12th SIAM International Conference on Data Mining, SDM 2012

Conference

Conference12th SIAM International Conference on Data Mining, SDM 2012
CountryUnited States
CityAnaheim, CA
Period12/4/2612/4/28

ASJC Scopus subject areas

  • Computer Science Applications

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  • Cite this

    Choo, J., Bohn, S., Nakamura, G. C., White, A. M., & Park, H. (2012). Heterogeneous data fusion via space alignment using nonmetric multidimensional scaling. In Proceedings of the 12th SIAM International Conference on Data Mining, SDM 2012 (pp. 177-188). (Proceedings of the 12th SIAM International Conference on Data Mining, SDM 2012). Society for Industrial and Applied Mathematics Publications. https://doi.org/10.1137/1.9781611972825.16