First IEEE International Conference on Data Mining (ICDM'01) San Jose, California November 29-December 02 ISBN: 0-7695-1119-8
Mining local structure is important in data analysis. Gaussian mixture is able to describe local structure through the covariance matrices, but when used on high dimensional data, fitly specifying such a large number of d(d + 1)=2 free elements in each covariance matrix is difficult. In this paper, by constraining the covariance matrix in decomposed orthonormal form, we propos a Local PCA algorithm to tackle this problem in help of RPCL competitive learning, which can automatically determine the number of local structure.
Citation:
Zhiyong Liu, Lei Xu, "RPCL-Based Local PCA Algorithm," icdm, pp.621, First IEEE International Conference on Data Mining (ICDM'01), 2001 Usage of this product signifies your acceptance of the Terms of Use. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||