Konferenzpaper

Erschienen in: 14th European symposium on artificial neural networks

Herausgeberliste: Verleysen, Michel

Jahr der Veröffentlichung: 2006

Seiten: 265-270

ISBN: 2-930307-06-4

URL: https://www.esann.org/sites/default/files/proceedings/legacy/es2006-97.pdf

Konferenz: 14th European Symposium on Artificial Neural Networks

Abstract: 

Multidimensional scaling (MDS) methods are designed to establish a one-to-one correspondence of input-output relationships. While the input may be given as high-dimensional data items or as adjacency matrix characterizing data relations, the output space is usually chosen as low-dimensional Euclidean, ready for visualization. MDSLocalize, an existing method, is reformulated in terms of Sanger’s rule that replaces the original foundations of computationally costly singular value decomposition. The derived method is compared to the recently proposed high-throughput multi-dimensional scaling (HiT-MDS) and to the well-established XGvis system. For comparison, real-value gene expression data and corresponding DNA sequences, given as proximity data, are considered.

Harvard-Zitierstil: Strickert, M., Sreenivasulu, N. and Seiffert, U. (2006) Sanger-driven MDSLocalize - a comparative study for genomic data, in Verleysen, M. (ed.) 14th European symposium on artificial neural networks. Evere: D-side. pp. 265-270. https://www.esann.org/sites/default/files/proceedings/legacy/es2006-97.pdf

APA-Zitierstil: Strickert, M., Sreenivasulu, N., & Seiffert, U. (2006). Sanger-driven MDSLocalize - a comparative study for genomic data. In Verleysen, M. (Ed.), 14th European symposium on artificial neural networks. (pp. 265-270). D-side. https://www.esann.org/sites/default/files/proceedings/legacy/es2006-97.pdf