Derives MSIP algorithm from MMD gradient flows for weighted quantization, extending mean shift and relating to preconditioned gradient descent and Lloyd's clustering.
A review of mean-shift algorithms for clustering
2 Pith papers cite this work. Polarity classification is still indexing.
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abstract
A natural way to characterize the cluster structure of a dataset is by finding regions containing a high density of data. This can be done in a nonparametric way with a kernel density estimate, whose modes and hence clusters can be found using mean-shift algorithms. We describe the theory and practice behind clustering based on kernel density estimates and mean-shift algorithms. We discuss the blurring and non-blurring versions of mean-shift; theoretical results about mean-shift algorithms and Gaussian mixtures; relations with scale-space theory, spectral clustering and other algorithms; extensions to tracking, to manifold and graph data, and to manifold denoising; K-modes and Laplacian K-modes algorithms; acceleration strategies for large datasets; and applications to image segmentation, manifold denoising and multivalued regression.
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UNVERDICTED 2representative citing papers
A Bayesian finite mixture of cluster-specific low-rank regressions for mixed Gaussian-Bernoulli-negative binomial outcomes, with posterior contraction results and WAIC-based tuning of clusters and rank.
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Weighted quantization using MMD: From mean field to mean shift via gradient flows
Derives MSIP algorithm from MMD gradient flows for weighted quantization, extending mean shift and relating to preconditioned gradient descent and Lloyd's clustering.
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Bayesian low-rank latent-cluster regression for mixed health outcomes
A Bayesian finite mixture of cluster-specific low-rank regressions for mixed Gaussian-Bernoulli-negative binomial outcomes, with posterior contraction results and WAIC-based tuning of clusters and rank.