Name
Abstract | ||
|---|---|---|
Kernel-based methods have experienced a substantial progress in the last years, tuning out an essential mechanism for data classification, clustering and pattern recognition. The effectiveness of kernel-based techniques, though, depends largely on the capability of the underlying kernel to properly embed data in the feature space associated to the kernel. However, visualizing how a kernel embeds the data in a feature space is not so straightforward, as the embedding map and the feature space are implicitly defined by the kernel. In this work, we present a novel technique to visualize the action of a kernel, that is, how the kernel embeds data into a high-dimensional feature space. The proposed methodology relies on a solid mathematical formulation to map kernelized data onto a visual space. Our approach is faster and more accurate than most existing methods while still allowing interactive manipulation of the projection layout, a game-changing trait that other kernel-based projection techniques do not have. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/TVCG.2015.2464797 | Visualization and Computer Graphics, IEEE Transactions |
Keywords | Field | DocType |
Kernel Methods,Multidimensional Projection,Visualization | Kernel (linear algebra),Graph kernel,Computer vision,Pattern recognition,Radial basis function kernel,Computer science,Kernel embedding of distributions,Tree kernel,Polynomial kernel,Artificial intelligence,String kernel,Kernel method | Journal |
Volume | Issue | ISSN |
PP | 99 | 1077-2626 |
Citations | PageRank | References |
3 | 0.36 | 23 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| A. C. Barbosa | 1 | 14 | 3.65 |
| Fernando Vieira Paulovich | 2 | 87 | 4.22 |
| Afonso Paiva | 3 | 135 | 16.76 |
| Siome Goldenstein | 4 | 618 | 47.43 |