Generalized Leverage Scores: Geometric Interpretation and Applications

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussaConference article in proceedingsScientificvertaisarvioitu

1 Sitaatiot (Scopus)
36 Lataukset (Pure)

Abstrakti

In problems involving matrix computations, the concept of leverage has found a large number of applications. In particular, leverage scores, which relate the columns of a matrix to the subspaces spanned by its leading singular vectors, are helpful in revealing column subsets to approximately factorize a matrix with quality guarantees. As such, they provide a solid foundation for a variety of machine-learning methods. In this paper we extend the definition of leverage scores to relate the columns of a matrix to arbitrary subsets of singular vectors. We establish a precise connection between column and singular-vector subsets, by relating the concepts of leverage scores and principal angles between subspaces. We employ this result to design approximation algorithms with provable guarantees for two well-known problems: generalized column subset selection and sparse canonical correlation analysis. We run numerical experiments to provide further insight on the proposed methods. The novel bounds we derive improve our understanding of fundamental concepts in matrix approximations. In addition, our insights may serve as building blocks for further contributions.
AlkuperäiskieliEnglanti
OtsikkoProceedings of the 39th International Conference on Machine Learning
KustantajaJMLR
Sivut17056-17070
TilaJulkaistu - 2022
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaInternational Conference on Machine Learning - Baltimore, Yhdysvallat
Kesto: 17 heinäk. 202223 heinäk. 2022
Konferenssinumero: 39

Julkaisusarja

NimiProceedings of Machine Learning Research
KustantajaPMLR
Vuosikerta162
ISSN (elektroninen)2640-3498

Conference

ConferenceInternational Conference on Machine Learning
LyhennettäICML
Maa/AlueYhdysvallat
KaupunkiBaltimore
Ajanjakso17/07/202223/07/2022

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