Automatic Hyperparameter Tuning in Sparse Matrix Factorization

Autor:	Kawasumi, Ryota, Takeda, Koujin
Rok vydání:	2023
Předmět:	Statistics - Machine Learning Condensed Matter - Disordered Systems and Neural Networks Computer Science - Information Theory Computer Science - Machine Learning
Zdroj:	Neural Computation (2023) 35 (6) 1086-1099
Druh dokumentu:	Working Paper
DOI:	10.1162/neco_a_01581
Popis:	We study the problem of hyperparameter tuning in sparse matrix factorization under Bayesian framework. In the prior work, an analytical solution of sparse matrix factorization with Laplace prior was obtained by variational Bayes method under several approximations. Based on this solution, we propose a novel numerical method of hyperparameter tuning by evaluating the zero point of normalization factor in sparse matrix prior. We also verify that our method shows excellent performance for ground-truth sparse matrix reconstruction by comparing it with the widely-used algorithm of sparse principal component analysis. Comment: 13 pages, 5 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2305.10114 Zobrazit plný text záznamu Plný text ve formátu PDF