all AI news
On the use of the Gram matrix for multivariate functional principal components analysis
June 21, 2024, 4:54 a.m. | Steven Golovkine, Edward Gunning, Andrew J. Simpkin, Norma Bargary
stat.ML updates on arXiv.org arxiv.org
Abstract: Dimension reduction is crucial in functional data analysis (FDA). The key tool to reduce the dimension of the data is functional principal component analysis. Existing approaches for functional principal component analysis usually involve the diagonalization of the covariance operator. With the increasing size and complexity of functional datasets, estimating the covariance operator has become more challenging. Therefore, there is a growing need for efficient methodologies to estimate the eigencomponents. Using the duality of the space …
abstract analysis arxiv components covariance data data analysis fda functional key matrix multivariate reduce replace stat.me stat.ml the key tool type
More from arxiv.org / stat.ML updates on arXiv.org
Jobs in AI, ML, Big Data
AI Focused Biochemistry Postdoctoral Fellow
@ Lawrence Berkeley National Lab | Berkeley, CA
Senior Data Engineer
@ Displate | Warsaw
Senior Backend Eng for the Cloud Team - Yehud or Haifa
@ Vayyar | Yehud, Center District, Israel
Business Applications Administrator (Google Workspace)
@ Allegro | Poznań, Poland
Backend Development Technical Lead (Demand Solutions) (f/m/d)
@ adjoe | Hamburg, Germany
Front-end Engineer
@ Cognite | Bengaluru