Non-Negative Sparse Principal Component Analysis
Corressponding author's email:
thanhddx@tut.edu.vnKeywords:
principal component analysis, semi-definite relaxation, semi-definite programming, 1 -minimization, iterative reweighting, bisection algorithmAbstract
With applications throughout science and engineering, sparse principal component analysis considers the problem of maximizing the variance explained by a particular linear combination of the input variables where the number ofnonzero coefficients is constrained. In this paper, we consider nonnegative sparse principal component analysis in which the coeficients in the combination are required to be non-negative. We improve our recent results by applying an appropriate re-weighted algorithms. Numerical results illustrate the improvement.
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