A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2010; you can also visit the original URL.
The file type is
Proceedings of 2004 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.04EX826)
Isomap is a recently proposed algorithm for manifold learning and nonlinear dimensionality reduction. In the Isomap algorithm, geodesic distances between points are extracted instead of simply taking the Euclidean distance, thus a geometric distance graph is constructed and the embedding is obtained from the graph. However, when this method is applied into multi-class data, several isolated sub-graphs will form thus desirable embedding cannot be achieved. In this paper, an extended Isomapdoi:10.1109/icmlc.2004.1380379 fatcat:kfcil3pv6raslbtnvn7bhn73ky