Nonnegative matrix factorization
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(Difference between revisions)
| Line 9: | Line 9: | ||
by solving | by solving | ||
| - | <math>\begin{array}{cl} | + | <math>\begin{array}{cl}{\text 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\\ |
| - | + | {\text 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\\ | &W\geq0\\ | ||
&H\geq0\\ | &H\geq0\\ | ||
| - | & | + | &{\text rank}\,Z\leq2\end{array}</math> |
which follows from the fact, at optimality, | which follows from the fact, at optimality, | ||
| Line 30: | Line 30: | ||
<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\\ | <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\\ | + | \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\\ | &W\geq0\\ | ||
&H\geq0\end{array}</math> | &H\geq0\end{array}</math> | ||
Current revision
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.