Building connected neighborhood graphs for isometric data embedding
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Data embedding techniques and applications
Proceedings of the 2nd international workshop on Computer vision meets databases
Improving geodesic distance estimation based on locally linear assumption
Pattern Recognition Letters
Extending metric multidimensional scaling with Bregman divergences
Pattern Recognition
Extending Sammon mapping with Bregman divergences
Information Sciences: an International Journal
Hi-index | 0.01 |
Sammon's nonlinear mapping (NLM) is an iterative procedure to project high dimensional data into low dimensional configurations. This paper discusses NLM using geodesic distances and proposes a mapping method GeoNLM. We compare its performance through experiments to the performances of NLM and Isomap. It is found that both GeoNLM and Isomap can unfold data manifolds better than NLM. GeoNLM outperforms Isomap when the short-circuit problem occurs in computing the neighborhood graph of data points. In turn, Isomap outperforms GeoNLM if the neighborhood graph is correctly constructed. These observations are discussed to reveal the features of geodesic distance estimation by graph distances.