Title :
Estimating manifold dimension with nearest neighbor graphs
Author_Institution :
Dept. of Inf. Manage., Hunan Coll. of Finance & Econ., Changsha, China
Abstract :
We introduce a new algorithm to estimate the manifold dimension of datasets. Our technique is based on the idea that the manifold structure should be preserved as far as possible after dimension reduction. Computing the structure errors of nearest neighbor graphs before and after dimension reduction, we gain the manifold dimension by seeking the smallest value of dimension when the structure error is equal to zero. Experiments show that our results is consistent with the results obtained by other methods, but our method doesn´t depend on the success of both the dimensionality reduction algorithm and the method used to discover the inverse map.
Keywords :
estimation theory; graph theory; dimensionality reduction algorithm; manifold dimension estimation; nearest neighbor graph; structure error; Educational institutions; Euclidean distance; Finance; Information management; Nearest neighbor searches; Speech; High dimension data; manifold dimension; manifold learning; manifold structure; nearest neighbor graph;
Conference_Titel :
Intelligent Computing and Intelligent Systems, 2009. ICIS 2009. IEEE International Conference on
Conference_Location :
Shanghai
Print_ISBN :
978-1-4244-4754-1
Electronic_ISBN :
978-1-4244-4738-1
DOI :
10.1109/ICICISYS.2009.5357770