One-step Bipartite Graph Cut: A Normalized Formulation and Its Application to Scalable Subspace Clustering. (arXiv:2305.07386v1 [cs.LG])

The bipartite graph structure has shown its promising ability in facilitating
the subspace clustering and spectral clustering algorithms for large-scale
datasets. To avoid the post-processing via k-means during the bipartite graph
partitioning, the constrained Laplacian rank (CLR) is often utilized for
constraining the number of connected components (i.e., clusters) in the
bipartite graph, which, however, neglects the distribution (or normalization)
of these connected components and may lead to imbalanced or even ill clusters.
Despite the significant success of normalized cut …

algorithms application arxiv clustering datasets graph k-means partitioning processing scalable scale