Implicit Annealing in Kernel Spaces: A Strongly Consistent Clustering Approach

Paul, Debolina; Chakraborty, Saptarshi; Das, Swagatam; Xu, Jason L.

doi:10.1109/tpami.2022.3217137

Cited by 2 publications

(1 citation statement)

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“…Recent advancements in convex or concave fusion methods have shown promise in enhancing stability, achieving more consistent global or local optimality and reliable estimation of cluster centers and counts through sparseinducing penalties on pairwise centers [6][7][8][9]. For clustering high-dimensional data, the data can be mapped into a high-dimensional feature space (kernel space) for processing [10], or clustering can be achieved by optimizing a smooth and continuous objective function that is based on robust statistics [11]. This paper introduces a comprehensive empirical validation of these methods across simulation studies and real data analysis, detailing their improved stability over traditional methods and the practical implications of these advancements.…”

Section: Introductionmentioning

confidence: 99%

Fast Fusion Clustering via Double Random Projection

Wang,

Li,

Zhou

et al. 2024

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In unsupervised learning, clustering is a common starting point for data processing. The convex or concave fusion clustering method is a novel approach that is more stable and accurate than traditional methods such as k-means and hierarchical clustering. However, the optimization algorithm used with this method can be slowed down significantly by the complexity of the fusion penalty, which increases the computational burden. This paper introduces a random projection ADMM algorithm based on the Bernoulli distribution and develops a double random projection ADMM method for high-dimensional fusion clustering. These new approaches significantly outperform the classical ADMM algorithm due to their ability to significantly increase computational speed by reducing complexity and improving clustering accuracy by using multiple random projections under a new evaluation criterion. We also demonstrate the convergence of our new algorithm and test its performance on both simulated and real data examples.

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