Spatial Transformer K-Means

Romain Cosentino; Randall Balestriero; Yanis Bahroun; Anirvan Sengupta; Richard Baraniuk; Behnaam Aazhang

doi:10.1109/IEEECONF56349.2022.10064695

Spatial Transformer K-Means

Romain Cosentino, Randall Balestriero, Yanis Bahroun, Anirvan Sengupta, Richard Baraniuk, Behnaam Aazhang

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

The K-means algorithm is one of the most employed centroid-based clustering algorithms. Unfortunately, it often requires intricate data embeddings for good performance, which comes at the cost of reduced theoretical guarantees and loss of interpretability. Instead, we propose to use 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, making the clustering task simpler and improving performance, leading to state-of-theart results, (ii) clustering in the input space of the data, providing a fully interpretable clustering algorithm, and (iii) the benefit of convergence guarantees.

Original language	English (US)
Title of host publication	56th Asilomar Conference on Signals, Systems and Computers, ACSSC 2022
Editors	Michael B. Matthews
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1444-1448
Number of pages	5
ISBN (Electronic)	9781665459068
DOIs	https://doi.org/10.1109/IEEECONF56349.2022.10064695
State	Published - 2022
Event	56th Asilomar Conference on Signals, Systems and Computers, ACSSC 2022 - Virtual, Online, United States Duration: Oct 31 2022 → Nov 2 2022

Publication series

Name	Conference Record - Asilomar Conference on Signals, Systems and Computers
Volume	2022-October
ISSN (Print)	1058-6393

Conference

Conference	56th Asilomar Conference on Signals, Systems and Computers, ACSSC 2022
Country/Territory	United States
City	Virtual, Online
Period	10/31/22 → 11/2/22

Keywords

K-means
Spatial transformer
Symmetry
Thin plate spline interpolation

ASJC Scopus subject areas

Signal Processing
Computer Networks and Communications

Access to Document

10.1109/IEEECONF56349.2022.10064695

Cite this

Cosentino, R., Balestriero, R., Bahroun, Y., Sengupta, A., Baraniuk, R., & Aazhang, B. (2022). Spatial Transformer K-Means. In M. B. Matthews (Ed.), 56th Asilomar Conference on Signals, Systems and Computers, ACSSC 2022 (pp. 1444-1448). (Conference Record - Asilomar Conference on Signals, Systems and Computers; Vol. 2022-October). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IEEECONF56349.2022.10064695

Spatial Transformer K-Means. / Cosentino, Romain; Balestriero, Randall; Bahroun, Yanis et al.
56th Asilomar Conference on Signals, Systems and Computers, ACSSC 2022. ed. / Michael B. Matthews. Institute of Electrical and Electronics Engineers Inc., 2022. p. 1444-1448 (Conference Record - Asilomar Conference on Signals, Systems and Computers; Vol. 2022-October).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Cosentino, R, Balestriero, R, Bahroun, Y, Sengupta, A, Baraniuk, R & Aazhang, B 2022, Spatial Transformer K-Means. in MB Matthews (ed.), 56th Asilomar Conference on Signals, Systems and Computers, ACSSC 2022. Conference Record - Asilomar Conference on Signals, Systems and Computers, vol. 2022-October, Institute of Electrical and Electronics Engineers Inc., pp. 1444-1448, 56th Asilomar Conference on Signals, Systems and Computers, ACSSC 2022, Virtual, Online, United States, 10/31/22. https://doi.org/10.1109/IEEECONF56349.2022.10064695

Cosentino R, Balestriero R, Bahroun Y, Sengupta A, Baraniuk R, Aazhang B. Spatial Transformer K-Means. In Matthews MB, editor, 56th Asilomar Conference on Signals, Systems and Computers, ACSSC 2022. Institute of Electrical and Electronics Engineers Inc. 2022. p. 1444-1448. (Conference Record - Asilomar Conference on Signals, Systems and Computers). doi: 10.1109/IEEECONF56349.2022.10064695

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AB - The K-means algorithm is one of the most employed centroid-based clustering algorithms. Unfortunately, it often requires intricate data embeddings for good performance, which comes at the cost of reduced theoretical guarantees and loss of interpretability. Instead, we propose to use 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, making the clustering task simpler and improving performance, leading to state-of-theart results, (ii) clustering in the input space of the data, providing a fully interpretable clustering algorithm, and (iii) the benefit of convergence guarantees.

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Spatial Transformer K-Means

Abstract

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Conference

Keywords

ASJC Scopus subject areas

Access to Document

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