Spatial Transformer K-Means

• URL: http://arxiv.org/abs/2202.07829v1
• Date: Wed, 16 Feb 2022 02:25:46 GMT
• Title: Spatial Transformer K-Means
• Authors: Romain Cosentino, Randall Balestriero, Yanis Bahroun, Anirvan Sengupta, Richard Baraniuk, Behnaam Aazhang
• Abstract summary: Intricate data embeddings have been designed to push $K$-means performances. We propose preserving the intrinsic data space and augment K-means with a similarity measure invariant to non-rigid transformations.
• Score: 16.775789494555017
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: K-means defines one of the most employed centroid-based clustering algorithms with performances tied to the data's embedding. Intricate data embeddings have been designed to push $K$-means performances at the cost of reduced theoretical guarantees and interpretability of the results. Instead, we propose preserving the intrinsic data space and augment K-means with a similarity measure invariant to non-rigid transformations. This enables (i) the reduction of intrinsic nuisances associated with the data, reducing the complexity of the clustering task and increasing performances and producing state-of-the-art results, (ii) clustering in the input space of the data, leading to a fully interpretable clustering algorithm, and (iii) the benefit of convergence guarantees.

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