Correlation Clustering

By Anthony Wirth. 2017

743 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Probabilistic & Bayesian Methods, Algorithms & Complexity

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Citations in the corpus

InstanceCut: From Edges to Instances with MultiCut (2016, methodology): “Our problem formulation consists of two components: (i) a conditional random field model [27] and (ii) a graph partition problem, known as MultiCut [12] or correlation clustering [4].”
DeeperCut: A Deeper, Stronger, and Faster Multi-person Pose Estimation Model (2016, neutral): “The DeepCut ILP is hard and hard to approximate, as it generalizes the minimum cost multicut or correlation clustering problem which is APX-hard [24,25].”
Efficient Decomposition of Image and Mesh Graphs by Lifted Multicuts (2015, background): “One disadvantage is the NP-hardness of the MP [11, 18].”
Features for Multi-target Multi-camera Tracking and Re-identification (2018, methodology): “We choose correlation clustering [4, 57, 58] to trade off computational cost for simplicity of formulation and accuracy.”
Edge-Labeling Graph Neural Network for Few-Shot Learning (2019, background): “Edge-Labeling Graph Correlation clustering (CC) is a graph-partitioning algorithm [40] that infers the edge labels of the graph by simultaneously maximizing intracluster similarity and inter-cluster dissimilarity.”
Subgraph decomposition for multi-target tracking (2015, methodology): “The Minimum Cost Subgraph Multicut Problem is NP-hard [4] and APXhard [7].”
Tracking the Same Neurons across Multiple Days in Ca2+ Imaging Data (2017, methodology): “…in (I)–(M) were obtained from the Allen recorded on three different days in the visual cortex of head-fixed behaving mice 1108 Cell Reports 21, 1102–1115, October 24, 2017 multiple sessions as single entities, we applied a clustering procedure (Bansal et al., 2004) based on the estimated Psame.”
Performance Measures and a Data Set for Multi-target, Multi-camera Tracking (2016, background): “A Binary Integer Program (BIP) solves the correlation clustering problem [59] on G: Partition V so as to maximize the sum of the correlations wij assigned to edges that connect coidentical observations and the penalties −wij assigned to edges that straddle identities.”
Discriminative clustering for market segmentation (2012, methodology): “We can alleviate this by randomly partitioning the examples in the least-squares estimation in Equation (8) into s disjoint subsets S((1)), .”
Motion Trajectory Segmentation via Minimum Cost Multicuts (2015, background): “Although problem defined in equation (1) is NP-hard [5] and even APX-hard [11], instances have been solved within tight bounds or even to optimality in for example [2] using the constrained integer linear programming (ILP) problem formulation from equation 1 and branch-and-cut.”