# Nonnegative matrix factorization

(Difference between revisions)
 Revision as of 02:44, 17 February 2010 (edit) (ruqKKcWrG)← Previous diff Revision as of 04:24, 17 February 2010 (edit) (undo)m (Reverted edits by 41.190.16.17 (Talk); changed back to last version by Ranjelin)Next diff → Line 1: Line 1: - xA3hHv fhanfeusylsr, [url=http://idhijwizkysp.com/]idhijwizkysp[/url], [link=http://cxgygcljwequ.com/]cxgygcljwequ[/link], http://ogpjkfcdsjji.com/ + Exercise from [http://meboo.convexoptimization.com/Meboo.html Convex Optimization & Euclidean Distance Geometry], ch.4: + + Given rank-2 nonnegative matrix + X=\!\left[\!\begin{array}{ccc}17&28&42\\ + 16&47&51\\ + 17&82&72\end{array}\!\right], + + find a nonnegative factorization + X=WH\,[/itex] + by solving + + $\begin{array}{cl}\mbox{find}_{A\in\mathbb{S}^3,\,B\in\mathbb{S}^3,\,W\in\mathbb{R}^{3\times2},\,H\in\mathbb{R}^{2\times3}}&W\,,\,H\\ + \mbox{subject to}&Z=\left[\begin{array}{ccc}I&W^{\rm T}&H\\W&A&X\\H^{\rm T}&X^{\rm T}&B\end{array}\right]\succeq0\\ + &W\geq0\\ + &H\geq0\\ + &\mbox{rank}\,Z\leq2\end{array} + + which follows from the fact, at optimality, + + [itex] Z^\star=\left[\!\begin{array}{c}I\\W\\H^{\rm T}\end{array}\!\right]\begin{array}{c}\textbf{[}\,I~~W^{\rm T}~H\,\textbf{]} + \end{array}$ + + Use the known closed-form solution for a direction vector $Y\,$ to regulate rank (rank constraint is replaced) by [[Convex Iteration]]; + + set $_{}Z^\star\!=Q\Lambda Q^{\rm T}\!\in\mathbb{S}^\mathbf{8}$ to a nonincreasingly ordered diagonalization and + [itex]_{}U^\star\!=_{\!}Q(:\,,_{^{}}3\!:\!8)\!\in_{\!}\reals^{\mathbf{8}\times\mathbf{6}}, + then [itex]Y\!=U^\star U^{\star\rm T}. + +
+ In summary, initialize [itex]Y=I\, then alternate solution of + + [itex]\begin{array}{cl}\mbox{minimize}_{A\in\mathbb{S}^3,\,B\in\mathbb{S}^3,\,W\in\mathbb{R}^{3\times2},\,H\in\mathbb{R}^{2\times3}}&\langle Z\,,Y\rangle\\ + \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\end{array} + + with + + [itex]Y\!=U^\star U^{\star\rm T}. + Global convergence occurs, in this example, in only a few iterations.

## Revision as of 04:24, 17 February 2010

Exercise from Convex Optimization & Euclidean Distance Geometry, ch.4:

Given rank-2 nonnegative matrix $LaTeX: X=\!\left[\!\begin{array}{ccc}17&28&42\\

 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 a nonincreasingly ordered diagonalization and $LaTeX: _{}U^\star\!=_{\!}Q(:\,,_{^{}}3\!:\!8)\!\in_{\!}\reals^{\mathbf{8}\times\mathbf{6}}$, then $LaTeX: Y\!=U^\star U^{\star\rm T}.$

In summary, initialize $LaTeX: Y=I\,$ then alternate solution of

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

with

$LaTeX: Y\!=U^\star U^{\star\rm T}.$ Global convergence occurs, in this example, in only a few iterations.