RPCL-Based Local PCA Algorithm
نویسندگان
چکیده
Mining local structure is important in data analysis. Gaussian mixture is able to describe local structure through the covariance matrices, but when used on highdimensional 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 propose a Local PCA algorithm to tackle this problem in help of RPCL competitive learning, which can automatically determine the number of local structures.
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