All talks will be held in lecture hall H6 at the western end of the main hall.
The time for a talk is 20 minutes plus 5 min discussion.
Thursday, 2007-09-06 - from 10:30 to 12:15
The self-organizing map as a visual neighbor retrieval method
|Presenting Author: Kristian Nybo|
|Authors: Kristian Nybo, Jarkko Venna, Samuel Kaski|
We have recently introduced rigorous goodness criteria for information
visualization by posing it as a visual neighbor retrieval problem, where the
task is to find proximate high-dimensional data based only on a low-dimensional
display. Standard information retrieval criteria such as precision and recall
can then be used for information visualization. We introduced an algorithm,
Neighbor Retrieval Visualizer (NeRV), to optimize the total cost of retrieval
errors. NeRV was shown to outperform alternative methods, but the SOM was not
included in the comparison. In empirical experiments of this paper the SOM
turns out to be comparable to the best methods in terms of (smoothed) precision
but not on recall. On a related measure called trustworthiness, the SOM
outperforms all others. Finally, we suggest that for information visualization
tasks the free parameters of the SOM could be optimized for information
visualization with cross-validation.
Component Selection for the Metro Visualisation of the Self-Organising Map
|Presenting Author: Robert Neumayer|
|Authors: Robert Neumayer, Rudolf Mayer, Andreas Rauber|
Self-Organising Maps have been used for a wide range of clustering applications. They are well-suited for various visualisation techniques to offer better insight into the clustered data sets. A particularly feasible visualisation is the plotting of single components of a data set and their distribution across the SOM. One central problem of the visualisation of Component Planes is that a single plot is needed for each component; this understandably leads to problems with higher-dimensional data. We therefore build on the Metro Visualisation for Self-Organising Maps which integrates the idea of Component Planes into one illustration. Higher-dimensional data sets still pose problems in terms of overloaded visualisations -- component selection and aggregation techniques are highly desirable. We therefore propose and compare two methods, one for the aggregation of correlated components, one for the selection of the components most feasible for visualisation for a given clustering.
An Adaptive Multidimensional Scaling and Principled Nonlinear Manifold
|Presenting Author: Hujun Yin|
|Authors: Hujun Yin|
The self-organizing map (SOM) and some of its variants such as visualization induced SOM (ViSOM) have been shown to yield similar results to multidimensional scaling (MDS). However the exact connection has yet been established. In this paper we first examine their relationship with (generalized) MDS from their cost functions in the aspect of data visualization and dimensionality reduction. The SOM is shown to produce a quantized, qualitative or nonmetric scaling and while the ViSOM is a quantitative metric scaling. Then we propose a way to use the core principle of the ViSOM, i.e. local distance preserving, to adaptively and incrementally construct a metric local scaling and to extract nonlinear manifold. Comparison with other methods such as ISOMAP and LLE has been made, especially in mapping highly nonlinear subspaces. The advantages over other methods are also discussed.
Classification of similar productivity zones in the sugar cane culture using clustering of SOM component planes based on the SOM distance matrix
|Presenting Author: Miguel Arturo Barreto-Sanz|
|Authors: Miguel A. Barreto-Sanz and Andres Pérez-Uribe|
A technique called component planes is commonly used to visualize variables behavior with Self Organizing Map (SOM). A methodology to clustering the component planes based on the SOM distance matrix is presented. This methodology is used in order to classify zones with similar agro-ecological conditions in the sugar cane culture. Analyzing the obtained groups it was possible to extract new knowledge about the relationship between the agro-ecological variables and productivity.