Konferenzpaper

Jahr der Veröffentlichung: 2005

Seiten: 625-633

Zeitschrift: Lecture notes in computer science

Bandnummer: 3696

ISSN: 0302-9743

ISBN: 3-540-28752-3

eISSN: 1611-3349

DOI Link: https://doi.org/10.1007/11550822_97

Konferenz: 15th International Conference on Artificial Neural Networks (ICANN 2005)

Verlag: Springer

Serientitel: Lecture Notes in Computer Science

Abstract: 
Multidimensional Scaling (MDS) is a powerful dimension reduction technique for embedding high-dimensional data into a low-dimensional target space. Thereby, the distance relationships in the source are reconstructed in the target space as best as possible according to a given embedding criterion. Here, a new stress function with intuitive properties and a very good convergence behavior is presented. Optimization is combined with an efficient implementation for calculating dynamic distance matrix correlations, and the implementation can be transferred to other related algorithms. The suitability of the proposed MDS for high-throughput data (HiT-MDS) is studied in applications to macroarray analysis for up to 12,000 genes.

Harvard-Zitierstil: Strickert, M., Teichmann, S., Sreenivasulu, N. and Seiffert, U. (2005) High-throughput multi-dimensional scaling (HiT-MDS) for cDNA-array expression data, Lecture notes in computer science, 3696, pp. 625-633. https://doi.org/10.1007/11550822_97

APA-Zitierstil: Strickert, M., Teichmann, S., Sreenivasulu, N., & Seiffert, U. (2005). High-throughput multi-dimensional scaling (HiT-MDS) for cDNA-array expression data. Lecture notes in computer science. 3696, 625-633. https://doi.org/10.1007/11550822_97