Geometrical homotopy for data visualization
(2015) ESANN 2015 - 23rd Eur. Symp. on Artificial Neural Networks, Computational Intelligence and Machine Learning — ISBN: [978-2-87587-014-8], p. 525-530, published
Files
ESANN2015-137_2.pdf
Open Access - Adobe PDF
- 430.86 KB
Details
- Authors
- Abstract
- This work presents an approach allowing for an interactive visualization of dimensionality reduction outcomes, which is based on an extended view of conventional homotopy. The pairwise functional followed from a simple ho- motopic function can be incorporated within a geometrical framework in order to yield a bi-parametric approach able to combine several kernel matrices. There- fore, the users can establish the mixture of kernels in an intuitive fashion by only varying two parameters. Our approach is tested by using kernel alternatives for conventional methods of spectral dimensional reduction such as multidimensional scalling, locally linear embedding and laplacian eigenmaps. Provided mixture rep- resents every single dimensional reduction approach as well as helps users to find a suitable representation of embedded data.
- Affiliations
Citations
Peluffo Ordoñez, D. H., Lee, J., Verleysen, M., & Alvarado-Pérez, J. C. (2015). Geometrical homotopy for data visualization. In ESANN 2015 - 23rd Eur. Symp. on Artificial Neural Networks, Computational Intelligence and Machine Learning (p. p. 525-530). D-side. https://hdl.handle.net/2078.5/253951