Partial Optimality in Cubic Correlation Clustering

02/09/2023
by   David Stein, et al.
0

The higher-order correlation clustering problem is an expressive model, and recently, local search heuristics have been proposed for several applications. Certifying optimality, however, is NP-hard and practically hampered already by the complexity of the problem statement. Here, we focus on establishing partial optimality conditions for the special case of complete graphs and cubic objective functions. In addition, we define and implement algorithms for testing these conditions and examine their effect numerically, on two datasets.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
02/21/2020

Parameterized Objectives and Algorithms for Clustering Bipartite Graphs and Hypergraphs

Graph clustering objective functions with tunable resolution parameters ...
research
02/18/2016

Efficient approaches for escaping higher order saddle points in non-convex optimization

Local search heuristics for non-convex optimizations are popular in appl...
research
03/15/2021

The Complexity of Checking Partial Total Positivity

We prove that checking if a partial matrix is partial totally positive i...
research
01/14/2021

Time-critical testing and search problems

This paper introduces a problem in which the state of a system needs to ...
research
12/01/2019

On the optimality of kernels for high-dimensional clustering

This paper studies the optimality of kernel methods in high-dimensional ...
research
02/11/2023

Partial k-means to avoid outliers, mathematical programming formulations, complexity results

A well-known bottleneck of Min-Sum-of-Square Clustering (MSSC, the celeb...
research
04/25/2019

The Mutex Watershed and its Objective: Efficient, Parameter-Free Image Partitioning

Image partitioning, or segmentation without semantics, is the task of de...

Please sign up or login with your details

Forgot password? Click here to reset