# Nonnegative matrix factorization

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- | + | Exercise from [http://meboo.convexoptimization.com/Meboo.html Convex Optimization & Euclidean Distance Geometry], ch.4: | |

+ | |||

+ | Given rank-2 nonnegative matrix | ||

+ | <math>X=\!\left[\!\begin{array}{ccc}17&28&42\\ | ||

+ | 16&47&51\\ | ||

+ | 17&82&72\end{array}\!\right],</math> | ||

+ | |||

+ | find a nonnegative factorization | ||

+ | <math> X=WH\,</math> | ||

+ | by solving | ||

+ | |||

+ | <math>\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}</math> | ||

+ | |||

+ | which follows from the fact, at optimality, | ||

+ | |||

+ | <math> 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}</math> | ||

+ | |||

+ | Use the known closed-form solution for a direction vector <math>Y\,</math> to regulate rank (rank constraint is replaced) by [[Convex Iteration]]; | ||

+ | |||

+ | set <math>_{}Z^\star\!=Q\Lambda Q^{\rm T}\!\in\mathbb{S}^\mathbf{8}</math> to a nonincreasingly ordered diagonalization and | ||

+ | <math>_{}U^\star\!=_{\!}Q(:\,,_{^{}}3\!:\!8)\!\in_{\!}\reals^{\mathbf{8}\times\mathbf{6}}</math>, | ||

+ | then <math>Y\!=U^\star U^{\star\rm T}.</math> | ||

+ | |||

+ | <br> | ||

+ | In summary, initialize <math>Y=I\,</math> then alternate solution of | ||

+ | |||

+ | <math>\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}</math> | ||

+ | |||

+ | with | ||

+ | |||

+ | <math>Y\!=U^\star U^{\star\rm T}.</math> | ||

+ | 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

find a nonnegative factorization by solving

which follows from the fact, at optimality,

Use the known closed-form solution for a direction vector to regulate rank (rank constraint is replaced) by Convex Iteration;

set to a nonincreasingly ordered diagonalization and , then

In summary, initialize then alternate solution of

with

Global convergence occurs, in this example, in only a few iterations.