2019/10/11 by Kohei Yoshikawa, Shuichi Kawano, Yoshikawa, Kohei +1
Computer Science · Engineering · Mathematics · #Advanced Statistical Methods and Models #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Methodology (stat.ME) #Sparse and Compressive Sensing Techniques #Statistical Methods and Inference #cs.LG #stat.ME #stat.ML
paper · pdf · doi:10.48550/arxiv.1910.05083
28 pages
openalex publication_date 2019/10/11 · arxiv created 2019/11/01 · arxiv updated 2019/11/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We consider the problem of constructing a reduced-rank regression model whose coefficient parameter is represented as a singular value decomposition with sparse singular vectors. The traditional estimation procedure for the coefficient parameter often fails when the true rank of the parameter is high. To overcome this issue, we develop an estimation algorithm with rank and variable selection via sparse regularization and manifold optimization, which enables us to obtain an accurate estimation of the coefficient parameter even if the true rank of the coefficient parameter is high. Using sparse regularization, we can also select an optimal value of the rank. We conduct Monte Carlo experiments and real data analysis to illustrate the effectiveness of our proposed method.