We show a branch and bound approach to exactly find the best sparse dimension reduction of a matrix. We can choose between enforcing orthogonality of the coefficients and uncorrelation of the components, and can explicitly set the degree of sparsity. We suggest methods to choose the number of non-zero loadings for each component; illustrate and compare our approach with existing methods through a benchmark data set. © Springer-Verlag 2009.
An exact approach to sparse principal component analysis / Farcomeni, Alessio. - In: COMPUTATIONAL STATISTICS. - ISSN 0943-4062. - 24:4(2009), pp. 583-604. [10.1007/s00180-008-0147-3]
An exact approach to sparse principal component analysis
FARCOMENI, Alessio
2009
Abstract
We show a branch and bound approach to exactly find the best sparse dimension reduction of a matrix. We can choose between enforcing orthogonality of the coefficients and uncorrelation of the components, and can explicitly set the degree of sparsity. We suggest methods to choose the number of non-zero loadings for each component; illustrate and compare our approach with existing methods through a benchmark data set. © Springer-Verlag 2009.File allegati a questo prodotto
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