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- W2999114689 abstract "Despite its well-known shortcomings, $k$-means remains one of the most widely used approaches to data clustering. Current research continues to tackle its flaws while attempting to preserve its simplicity. Recently, the textit{power $k$-means} algorithm was proposed to avoid trapping in local minima by annealing through a family of smoother surfaces. However, the approach lacks theoretical justification and fails in high dimensions when many features are irrelevant. This paper addresses these issues by introducing textit{entropy regularization} to learn feature relevance while annealing. We prove consistency of the proposed approach and derive a scalable majorization-minimization algorithm that enjoys closed-form updates and convergence guarantees. In particular, our method retains the same computational complexity of $k$-means and power $k$-means, but yields significant improvements over both. Its merits are thoroughly assessed on a suite of real and synthetic data experiments." @default.
- W2999114689 created "2020-01-23" @default.
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- W2999114689 date "2020-01-10" @default.
- W2999114689 modified "2023-10-16" @default.
- W2999114689 title "Entropy Regularized Power k-Means Clustering" @default.
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- W2999114689 doi "https://doi.org/10.48550/arxiv.2001.03452" @default.
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