Optimal graph edge weights driven nlms with multi-layer residual compensation

Multi-layer perceptrons with discrete weights

Cooperative Cuts: Graph Cuts with Submodular Edge Weights

We introduce a problem we call Cooperative cut, where the goal is to find a minimum-cost graph cut but where a submodular function is used to define the cost of a subsets of edges. That means, the cost of an edge that is added to the current cut set C depends on the edges in C. This generalization of the cost in the standard min-cut problem to a submodular cost function immediately makes the pr...

Notes on graph cuts with submodular edge weights

Generalizing the cost in the standard min-cut problem to a submodular cost function immediately makes the problem harder. Not only do we prove NP hardness even for nonnegative submodular costs, but also show a lower bound of Ω(|V |) on the approximation factor for the (s, t) cut version of the problem. On the positive side, we propose and compare three approximation algorithms with an overall a...

Robust Semi-Supervised Graph Classifier Learning with Negative Edge Weights

In a semi-supervised learning scenario, (possibly noisy) partially observed labels are used as input to train a classifier, in order to assign labels to unclassified samples. In this paper, we construct a complete graph-based binary classifier given only samples’ feature vectors and partial labels. Specifically, we first build appropriate similarity graphs with positive and negative edge weight...

Graph cuts with interacting edge weights: examples, approximations, and algorithms

We study an extension of the classical graph cut problem, wherein we replace the modular (sum of edge weights) cost function by a submodular set function defined over graph edges. Special cases of this problem have appeared in different applications in signal processing, machine learning, and computer vision. In this paper, we connect these applications via the generic formulation of “cooperati...