Manifold integration
From Wikipedia, the free encyclopedia
 |
This article has multiple issues. Please help improve it or discuss these issues on the talk page.
|
Manifold integration is a combined concept of manifold learning and data integration, or an extension of manifold learning for multiple measurements.
Various manifold learning methods have been developed. However, they consider only one dissimilarity matrix corresponding to one kernel matrix, which represents one manifold of the data set. In practice, however, we use multiple sensors at a time, and each sensor generates data set on one manifold. In such a case, manifold integration is a desirable task, combining these dissimilarity matrices into a compromise matrix that faithfully reflects multiple sensory information on one integrated manifold.
For more information, see [1]
[edit] Notes
 |
This article includes a list of references, related reading or external links, but its sources remain unclear because it lacks inline citations. Please improve this article by introducing more precise citations. (May 2009) |
- ^ H. Choi, S. Choi and Y. Choe, "Manifold Integration with Markov Random Walks," in Proc. 23rd Association for the Advancement of Artificial Intelligence (AAAI-08), Chicago, Illinois, July 13–17, 2008.
