An extended Isomap algorithm for learning multi-class manifold
Yiming Wu; Kap Luk Chan
Machine Learning and Cybernetics, 2004. Proceedings of 2004 International Conference on
Volume 6, Issue , 26-29 Aug. 2004 Page(s): 3429 - 3433 vol.6
Digital Object Identifier  
Summary: 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. An extended Isomap algorithm is proposed for the multi-class manifold learning which computes within-class and between-class geodesic distances separately and the final embedding is obtained from the augmented geodesic distance matrix using multidimensional scaling algorithm. Experimental results on synthetic and real data reveal the promising performance of the proposed method.

» View citation and abstract

Log in by entering your IEEE Web Account Username and Password.

IEEE Communications Society members: If you subscribe to the IEEE Electronic Periodicals Package or IEEE Electronic Periodicals Package Plus, you must access your subscription at www.comsoc.org.

Check with your librarian, information professional, or system manager to determine if you need to log in. Please complete the online Technical Support Form if you need assistance.

Select the Purchase History link to access the document. You will have 5 Days after purchase to access the Full Text PDF. Please complete the online Technical Support Form if you need assistance.

• Search and access Abstract records free of charge
• Register for table of contents alerts
• Purchase Full Text PDF documents

» Learn more about subscription options or how to become an IEEE Member.