1ERTOZ L, STEINBACH M, KUMAR V. Finding clusters of different sizes, shapes and densities in noisy high-dimensional data[ R]. Minnesota: Department of Computer Science, University of Minnesota, 2002.
2HAM J H, LEE D D, SAUL L K. Learning high-dimensional correspondences from low dimensional manifolds [ C ]//Proc of ICML Workshop on the Continuum from Labeled to Unlabeled Data in Machine Learning and Data Mining. Washington: [ s. n. ] , 2003:34-41.
3KOHONEN T. Self-organization and associated memory [ M]. [ S. l. ]: Springer-Verlag, 1988.
4KOHONEN T. Self-organizing maps [ M ]. New York: Spinger-Verlag, 2001.
5MINKA T P. Automatic choice of dimensionality for PCA[ C ]//Proc of International Conference on Advances in Neural Information Processing Systems. Cambridge: [ s. n. ] , 2001:598-604.
6GRIFFITHS T L, KALISH M L. A muhidimensional scaling approach to mental multiplication[ J ]. Memory & Cognition, 2002,30 ( 1 ) : 97-106.
7CAMASTRA F, VINCIARELLI A. Estimating the intrinsic dimension of data with a fractal-based method [J].IEEE Trans on Pattern Anal Mach Intell, 2002,24(10) :1404-1407.
8CAMASTRA F. Data dimension estimation methods: a survey[ J]. Pattern Recognition, 2003, 36:2945-2954.
9SCHOLKOPF B, SMOLA A, MULLER K. Nonlinear component analysis as a kernel eigenvalue problem [ J ]. Neural Computation, 1998,10(5) :1299-1319.
10TENENBAUM J B, De SILVA V, LANGFORD J C. A global geometric framework for nonlinear dimensionality reduction [ J ]. Science, 2000,290(5500) :2319-2323.
