Off-the-grid: Fast and Effective Hyperparameter Search for Kernel Clustering

Bruno Ordozgoiti Rubio, Lluís Belanche-Muñoz

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussaConference contributionScientificvertaisarvioitu

Abstrakti

Kernel functions are a powerful tool to enhance the k-means clustering algorithm via the kernel trick. It is known that the parameters of the chosen kernel function can have a dramatic impact on the result. In supervised settings, these can be tuned via cross-validation, but for clustering this is not straightforward and heuristics are usually employed. In this paper we study the impact of kernel parameters on kernel k-means. In particular, we derive a lower bound, tight up to constant factors, below which the parameter of the RBF kernel will render kernel k-means meaningless. We argue that grid search can be ineffective for hyperparameter search in this context and propose an alternative algorithm for this purpose. In addition, we offer an efficient implementation based on fast approximate exponentiation with provable quality guarantees. Our experimental results demonstrate the ability of our method to efficiently reveal a rich and useful set of hyperparameter values.
AlkuperäiskieliEnglanti
OtsikkoMachine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2020, Proceedings
ToimittajatFrank Hutter, Kristian Kersting, Jefrey Lijffijt, Isabel Valera
Sivut399-415
Sivumäärä17
Painos1
ISBN (elektroninen)9783030676612
DOI - pysyväislinkit
TilaJulkaistu - helmik. 2021
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisuussa
TapahtumaEuropean Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases - Virtual, Online
Kesto: 14 syysk. 202018 syysk. 2020
https://ecmlpkdd2020.net/

Julkaisusarja

NimiLecture Notes in Computer Science
KustantajaSpringer
Vuosikerta12458
ISSN (painettu)0302-9743
ISSN (elektroninen)1611-3349

Conference

ConferenceEuropean Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases
LyhennettäECML-PKDD
KaupunkiVirtual, Online
Ajanjakso14/09/202018/09/2020
www-osoite

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