Name
Abstract | ||
|---|---|---|
We derive a convex relaxation for cardinality constrained Principal Component Analysis (PCA) by using a simple representation of the L"1 unit ball and standard Lagrangian duality. The resulting convex dual bound is an unconstrained minimization of the sum of two nonsmooth convex functions. Applying a partial smoothing technique reduces the objective to the sum of a smooth and nonsmooth convex function for which an efficient first order algorithm can be applied. Numerical experiments demonstrate its potential. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1016/j.orl.2010.11.005 | Oper. Res. Lett. |
Keywords | Field | DocType |
simple representation,partial smoothing technique,convex approximation,unconstrained minimization,numerical experiment,standard lagrangian duality,principal component analysis,convex programming,unit ball,order algorithm,convex relaxation,sparse principal component analysis,nonsmooth convex function,lagrangian duality,convex function,first order | Combinatorics,Mathematical optimization,Convex combination,Convex hull,Convex set,Subderivative,Convex polytope,Proper convex function,Convex optimization,Convex analysis,Mathematics | Journal |
Volume | Issue | ISSN |
39 | 1 | Operations Research Letters |
Citations | PageRank | References |
6 | 0.55 | 8 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ronny Luss | 1 | 102 | 10.30 |
| Marc Teboulle | 2 | 5075 | 331.34 |