Journalartikel

Jahr der Veröffentlichung: 2014

Seiten: 97-109

Zeitschrift: Neurocomputing

Bandnummer: 141

ISSN: 0925-2312

eISSN: 1872-8286

DOI Link: https://doi.org/10.1016/j.neucom.2014.01.049

Verlag: Elsevier

Abstract: 
Neighbor-preserving embedding of relational data in low-dimensional Euclidean spaces is studied. Contrary to variants of stochastic neighbor embedding that minimize divergence measures between estimated neighborhood probability distributions, the proposed approach fits configurations in the output space by maximizing correlation with potentially asymmetric or missing relationships in the input space. In addition to the linear Pearson correlation measure, the use of soft formulations of Spearman and Kendall rank correlation is investigated for optimizing embeddings like 2D point cloud configurations. We illustrate how this scale-invariant correlation-based framework of multidimensional scaling (cbMDS) helps going beyond distance-preserving scaling approaches and how the embedding results are characteristically different from recent neighborhood embedding techniques. (C) 2014 Elsevier B.V. All rights reserved.

Harvard-Zitierstil: Strickert, M., Bunte, K., Schleif, F. and Huellermeier, E. (2014) Correlation-based embedding of pairwise score data, Neurocomputing, 141, pp. 97-109. https://doi.org/10.1016/j.neucom.2014.01.049

APA-Zitierstil: Strickert, M., Bunte, K., Schleif, F., & Huellermeier, E. (2014). Correlation-based embedding of pairwise score data. Neurocomputing. 141, 97-109. https://doi.org/10.1016/j.neucom.2014.01.049