Hotelling Deflation on Large Symmetric Spiked Tensors

Autor:	Seddik, Mohamed El Amine, Goulart, José Henrique de Morais, Guillaud, Maxime
Rok vydání:	2023
Předmět:	Statistics - Machine Learning Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	This paper studies the deflation algorithm when applied to estimate a low-rank symmetric spike contained in a large tensor corrupted by additive Gaussian noise. Specifically, we provide a precise characterization of the large-dimensional performance of deflation in terms of the alignments of the vectors obtained by successive rank-1 approximation and of their estimated weights, assuming non-trivial (fixed) correlations among spike components. Our analysis allows an understanding of the deflation mechanism in the presence of noise and can be exploited for designing more efficient signal estimation methods. Comment: 4 pages, 1 figure
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2304.10248 Zobrazit plný text záznamu View this record from Arxiv