Clustering
Fair Hierarchical Clustering
As machine learning has become more prevalent, researchers have begun to recognize the necessity of ensuring machine learning systems are fair.
Graduated Assignment for Joint Multi-Graph Matching and Clustering with Application to Unsupervised Graph Matching Network Learning Runzhong Wang 1,2 Junchi Y an 1,2 Xiaokang Y ang 2 1
Source code is publicly available as a module of ThinkMatch.
A Missing Details and Proofs We denote the degree of vertex v
We stress that unweighted and weighted in the linkage measure names refer to the linkage methods. Recall that our approach is based on geometric layering, where we group the edges based on their weights and process all edges within the same layer in parallel. A similar idea is used in the Affinity Clustering algorithm of Bateni et al. [ Our algorithm starts by first randomly coloring the active vertices red and blue with equal probability. Directly applying the random-mate approach (e.g., as applied in Let D be initialized to the identity clustering. O (log n) layers are required to represent every weight in this weight range.Lemma 2.1.
Hierarchical Agglomerative Graph Clustering in Poly-Logarithmic Depth
Each step replaces the two most similar clusters by its union.
No Subclass Left Behind: Fine-Grained Robustness in Coarse-Grained Classification Problems
We then use these approximate subclass labels as a form of noisy supervision in a distributionally robust optimization objective.
Stars: T era-Scale Graph Building for Clustering and Graph Learning
A fundamental procedure in the analysis of massive datasets is the construction of similarity graphs. Such graphs play a key role for many downstream tasks, including clustering, classification, graph learning, and nearest neighbor search.