| Paper ID | MLSP-9.5 |
| Paper Title |
SPARSITY IN MAX-PLUS ALGEBRA AND APPLICATIONS IN MULTIVARIATE CONVEX REGRESSION |
| Authors |
Nikos Tsilivis, National Technical University of Athens, Greece; Anastasios Tsiamis, University of Pennsylvania, United States; Petros Maragos, National Technical University of Athens, Greece |
| Session | MLSP-9: Learning Theory for Neural Networks |
| Location | Gather.Town |
| Session Time: | Tuesday, 08 June, 16:30 - 17:15 |
| Presentation Time: | Tuesday, 08 June, 16:30 - 17:15 |
| Presentation |
Poster
|
| Topic |
Machine Learning for Signal Processing: [MLR-LEAR] Learning theory and algorithms |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
In this paper, we study concepts of sparsity in the max-plus algebra and apply them to the problem of multivariate convex regression. We show how to efficiently find sparse (containing many −∞ elements) approximate solutions to max-plus equations by leveraging notions from submodular optimization. Subsequently, we propose a novel method for piecewise-linear surface fitting of convex multivariate functions, with optimality guarantees for the model parameters and an approximately minimum number of affine regions. |
