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https://doi.org/10.21256/zhaw-3565| Publication type: | Conference paper |
| Type of review: | Peer review (publication) |
| Title: | Generating low-dimensional denoised representations of nonlinear data with superparamagnetic agents |
| Authors: | Eggel, Thomas Christen, Markus Ott, Thomas |
| DOI: | 10.21256/zhaw-3565 |
| Proceedings: | Proceedings of the 2014 International Symposium on Nonlinear Theory and its Applications (NOLTA2014) |
| Page(s): | 180 |
| Pages to: | 183 |
| Conference details: | Nonlinear Theory and Applications 2014 (NOLTA), Luzern, 14-18 September 2014 |
| Issue Date: | 2014 |
| Publisher / Ed. Institution: | IEICE |
| Language: | English |
| Subjects: | Clustering; Dimensionality; Reduction |
| Subject (DDC): | 510: Mathematics |
| Abstract: | Visualisation of high-dimensional data by means of a low-dimensional embedding plays a key role in explorative data analysis. Classical approaches to dimensionality reduction, such as principal component analysis (PCA) and multidimensional scaling (MDS), struggle or even fail to reveal the relevant data characteristics when applied to noisy or nonlinear data structures. We present a novel approach for dimensionality reduction in combination with an automatic noise cleaning. By employing self-organising agents that are governed by the dynamics of the superparamagnetic clustering algorithm, the method is able to generate denoised low-dimensional embeddings for which the characteristics of nonlinear data structures are preserved or even emphasised. These properties are illustrated and compared to other approaches by means of toy and real-world examples. |
| Further description: | Copyright ©2016 IEICE |
| URI: | https://digitalcollection.zhaw.ch/handle/11475/4396 |
| Fulltext version: | Published version |
| License (according to publishing contract): | Licence according to publishing contract |
| Departement: | Life Sciences and Facility Management |
| Organisational Unit: | Institute of Computational Life Sciences (ICLS) |
| Appears in collections: | Publikationen Life Sciences und Facility Management |
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|---|---|---|---|---|
| A2L-D5-6207.pdf | 318.95 kB | Adobe PDF |  View/Open |
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Eggel, T., Christen, M., & Ott, T. (2014). Generating low-dimensional denoised representations of nonlinear data with superparamagnetic agents [Conference paper]. Proceedings of the 2014 International Symposium on Nonlinear Theory and Its Applications (NOLTA2014), 180–183. https://doi.org/10.21256/zhaw-3565
Eggel, T., Christen, M. and Ott, T. (2014) ‘Generating low-dimensional denoised representations of nonlinear data with superparamagnetic agents’, in Proceedings of the 2014 International Symposium on Nonlinear Theory and its Applications (NOLTA2014). IEICE, pp. 180–183. Available at: https://doi.org/10.21256/zhaw-3565.
T. Eggel, M. Christen, and T. Ott, “Generating low-dimensional denoised representations of nonlinear data with superparamagnetic agents,” in Proceedings of the 2014 International Symposium on Nonlinear Theory and its Applications (NOLTA2014), 2014, pp. 180–183. doi: 10.21256/zhaw-3565.
EGGEL, Thomas, Markus CHRISTEN und Thomas OTT, 2014. Generating low-dimensional denoised representations of nonlinear data with superparamagnetic agents. In: Proceedings of the 2014 International Symposium on Nonlinear Theory and its Applications (NOLTA2014). Conference paper. IEICE. 2014. S. 180–183
Eggel, Thomas, Markus Christen, and Thomas Ott. 2014. “Generating Low-Dimensional Denoised Representations of Nonlinear Data with Superparamagnetic Agents.” Conference paper. In Proceedings of the 2014 International Symposium on Nonlinear Theory and Its Applications (NOLTA2014), 180–83. IEICE. https://doi.org/10.21256/zhaw-3565.
Eggel, Thomas, et al. “Generating Low-Dimensional Denoised Representations of Nonlinear Data with Superparamagnetic Agents.” Proceedings of the 2014 International Symposium on Nonlinear Theory and Its Applications (NOLTA2014), IEICE, 2014, pp. 180–83, https://doi.org/10.21256/zhaw-3565.
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