@conference {42,
	title = {Metric representations of data via the Kernel-based Sammon Mapping},
	booktitle = {Neural Networks (IJCNN), The 2010 International Joint Conference on},
	year = {2010},
	note = {<p>Acceptance rate 71.5\% (621/868).</p>
},
	month = {July},
	publisher = {Institute of Electrical and Electronics Engineers (IEEE)},
	organization = {Institute of Electrical and Electronics Engineers (IEEE)},
	address = {Barcelona, Spain},
	abstract = {<p>In this paper we present a novel generalization of Sammon{\textquoteright}s Mapping (SM), which is a popular, metric multi-dimensional scaling technique used in data analysis and visualization. The new approach, namely the Kernel-based Sammon Mapping (KSM), yields the classic SM and other much related techniques as special cases. Apart from being able to approximate distance-preserving projections, it can also learn to metrically represent arbitrarily-defined dissimilarities or similarities between samples. Moreover, it can handle equally well numeric, categorical or mixed-type data. It is able to accomplish all this by modeling its projections as linear combinations of appropriate kernel functions. We report experimental results, which showcase KSM{\textquoteright}s capabilities in visually representing several meaningful relationships between samples of selected datasets.</p>
},
	keywords = {data analysis, data visualisation, data visualization, distance-preserving projections, kernel-based Sammon mapping, metric multidimensional scaling technique, metric representations, Visualization},
	doi = {10.1109/IJCNN.2010.5596662},
	author = {Mingbo Ma and Ryan Gonet and RuiZhi Yu and Georgios C. Anagnostopoulos}
}
