Nonnegative matrix factorization

From Wikimization

(Difference between revisions)
Jump to: navigation, search

Ranjelin (Talk | contribs)
(New page: Given rank-2 nonnegative matrix <math>X=\!\left[\!\begin{array}{ccc}17&28&42\\ 16&47&51\\ 17&82&72\end{array}...)
Next diff →

Revision as of 13:18, 28 September 2009

Given rank-2 nonnegative matrix LaTeX: X=\!\left[\!\begin{array}{ccc}17&28&42\\ </p> <pre> 16&47&51\\ 17&82&72\end{array}\!\right],

find a nonnegative factorization LaTeX: X=WH\, by solving

LaTeX: \begin{array}{cl}\mbox{find}_{A\in\mathbb{S}^3,\,B\in\mathbb{S}^3,\,W\in\mathbb{R}^{3\times2},\,H\in\mathbb{R}^{2\times3}}&W\,,\,H\\ \mbox{subject to}&Z=\left[\begin{array}{ccc}I&W^{\rm T}&H\\W&A&X\\H^{\rm T}&X^{\rm T}&B\end{array}\right]\succeq0\\ &W\geq0\\ &H\geq0\\ &\mbox{rank}\,Z\leq2\end{array}

which follows from the fact, at optimality,

LaTeX: Z^\star=\left[\!\begin{array}{c}I\\W\\H^{\rm T}\end{array}\!\right]\begin{array}{c}\textbf{[}\,I~~W^{\rm T}~H\,\textbf{]} \end{array}

Use the known closed-form solution for a direction vector LaTeX: Y\, to regulate rank (rank constraint is replaced) by convex iteration;

set LaTeX: _{}Z^\star\!=Q\Lambda Q^{\rm T}\!\in\mathbb{S}^\mathbf{8} to an ordered diagonalization and LaTeX: _{}U^\star\!=_{\!}Q(:\,,_{^{}}3\!:\!8)\!\in_{\!}\reals^{\mathbf{8}\times\mathbf{6}}, then LaTeX: Y\!=U^\star U^{\star\rm T}.

Retrieved from "http://www.convexoptimization.com/wikimization/index.php/Nonnegative_matrix_factorization"
Personal tools