Song, LeSmola, AlexGretton, ArthurBorgwardt, Karsten M.2025-06-292025-06-29http://www.scopus.com/inward/record.url?scp=34547972314&partnerID=8YFLogxKhttps://hdl.handle.net/1885/733765337We propose a family of clustering algorithms based on the maximization of dependence between the input variables and their cluster labels, as expressed by the Hilbert-Schmidt Independence Criterion (HSIC). Under this framework, we unify the geometric, spectral, and statistical dependence views of clustering, and subsume many existing algorithms as special cases (e.g. k-means and spectral clustering). Distinctive to our framework is that kernels can also be applied on the labels, which can endow them with particular structures. We also obtain a perturbation bound on the change in k-means clustering.8en200710.1145/1273496.127359934547972314