Iterative Spectral Method for Alternative Clustering

Chieh Wu, Stratis Ioannidis, Mario Sznaier, Xiangyu Li, David Kaeli, Jennifer Dy
Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics, PMLR 84:115-123, 2018.

Abstract

Given a dataset and an existing clustering as input, alternative clustering aims to find an alternative partition. One of the state-of-the-art approaches is Kernel Dimension Alternative Clustering (KDAC). We propose a novel Iterative Spectral Method (ISM) that greatly improves the scalability of KDAC. Our algorithm is intuitive, relies on easily implementable spectral decompositions, and comes with theoretical guarantees. Its computation time improves upon existing implementations of KDAC by as much as 5 orders of magnitude.

Cite this Paper

BibTeX
@InProceedings{pmlr-v84-wu18a, title = {Iterative Spectral Method for Alternative Clustering}, author = {Wu, Chieh and Ioannidis, Stratis and Sznaier, Mario and Li, Xiangyu and Kaeli, David and Dy, Jennifer}, booktitle = {Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics}, pages = {115--123}, year = {2018}, editor = {Storkey, Amos and Perez-Cruz, Fernando}, volume = {84}, series = {Proceedings of Machine Learning Research}, month = {09--11 Apr}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v84/wu18a/wu18a.pdf}, url = {https://proceedings.mlr.press/v84/wu18a.html}, abstract = {Given a dataset and an existing clustering as input, alternative clustering aims to find an alternative partition. One of the state-of-the-art approaches is Kernel Dimension Alternative Clustering (KDAC). We propose a novel Iterative Spectral Method (ISM) that greatly improves the scalability of KDAC. Our algorithm is intuitive, relies on easily implementable spectral decompositions, and comes with theoretical guarantees. Its computation time improves upon existing implementations of KDAC by as much as 5 orders of magnitude.} }
Endnote
%0 Conference Paper %T Iterative Spectral Method for Alternative Clustering %A Chieh Wu %A Stratis Ioannidis %A Mario Sznaier %A Xiangyu Li %A David Kaeli %A Jennifer Dy %B Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2018 %E Amos Storkey %E Fernando Perez-Cruz %F pmlr-v84-wu18a %I PMLR %P 115--123 %U https://proceedings.mlr.press/v84/wu18a.html %V 84 %X Given a dataset and an existing clustering as input, alternative clustering aims to find an alternative partition. One of the state-of-the-art approaches is Kernel Dimension Alternative Clustering (KDAC). We propose a novel Iterative Spectral Method (ISM) that greatly improves the scalability of KDAC. Our algorithm is intuitive, relies on easily implementable spectral decompositions, and comes with theoretical guarantees. Its computation time improves upon existing implementations of KDAC by as much as 5 orders of magnitude.
APA
Wu, C., Ioannidis, S., Sznaier, M., Li, X., Kaeli, D. & Dy, J.. (2018). Iterative Spectral Method for Alternative Clustering. Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 84:115-123 Available from https://proceedings.mlr.press/v84/wu18a.html.

Related Material