Dissimilarity representations are of interest when it is hard to define well-discriminating features for the raw measurements. For an exploration of such data, the techniques of multidimensional scaling (MDS) can be used. Given a symmetric dissimilarity matrix, they find a lower-dimensional configuration such that the distances are preserved. Here, Sammon nonlinear mapping is considered. In general, this iterative method must be recomputed when new examples are introduced, but its complexity is quadratic in the number of objects in each iteration step.
A simple modification to the nonlinear MDS, allowing for a significant reduction in complexity, is therefore considered, as well as a linear projection of the dissimilarity data. Now, generalization to new data can be achieved, which makes it suitable for solving classification problems. The linear and nonlinear mappings are then used in the setting of data visualization and classification. Our experiments show that the nonlinear mapping can be preferable for data inspection, while for discrimination purposes, a linear mapping can be recommended. Moreover, for the spatial lower-dimensional representation, a more global, linear classifier can be built, which outperforms the local nearest neighbor rule, traditionally applied to dissimilarities.
|Title of host publication||Structural, Syntactic, and Statistical Pattern Recognition, Proceedings|
|Editors||T Caelli, A Amin, RPW Duin, M Kamel, D de Ridder|
|Place of Publication||Berlin|
|Number of pages||9|
|Publication status||Published - 2002|
|Event||Joint IAPR International Workshops SSPR'02 and SPR'02 (Windsor, Canada) - Berlin|
Duration: 6 Aug 2002 → 9 Aug 2002
|Name||Lecture Notes in Computer Science|
|Conference||Joint IAPR International Workshops SSPR'02 and SPR'02 (Windsor, Canada)|
|Period||6/08/02 → 9/08/02|
- conference contrib. refereed
- ZX CWTS JFIS < 1.00