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Principal Component Pursuit with Reduced Linear Measurements
Principal Component Pursuit Reduced Linear Measurements low-rank matrix sparse matrix
2012/2/29
In this paper, we study the problem of decomposing a superposition of a low-rank matrix and a sparse matrix when a relatively few linear measurements are available. This problem arises in many data pr...
Projection-Pursuit Based Principal Component Analysis: a Large Sample Theory
Dispersion matrices eigenvalues and eigenvectors empirical processes principal component analysis projection pursuit (PP)
2007/12/10
摘要 The principal component analysis (PCA) is one of the most celebrated methods in analysing multivariate data. An effort of extending PCA is projection pursuit (PP), a more general class of dimension...
ASYMPTOTIC THEORIES FOR THE ROBUST PP ESTIMATORS OF THE PRINCIPAL COMPONENTS AND DISPERSION MATRIX Ⅲ.BOOTSTRAP CONFIDENCE SETS,BOOTSTRAP TESTS
Bootstrap confidence sets eigenvalues an
2007/8/7
In this paper the bootstrap theories, which are based on the author's former paper, of M-typ eprincipal components and dispersion matrices and M-type PP tests for multivariate locationand scale are ob...