Automatic Hyperparameter Tuning in Sparse Matrix Factorization
Ryota Kawasumi Koujin Takeda
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.