58 citations · 336 across the 38 of their papers we have counts for
9 papers · 1 filter
On Model Selection Consistency of Lasso for High-Dimensional Ising Models
Xiangming Meng, Tomoyuki Obuchi, Yoshiyuki Kabashima
We theoretically analyze the model selection consistency of least absolute shrinkage and selection operator (Lasso), both with and without post-thresholding, for high-dimensional I…
Reconstructing Sparse Signals via Greedy Monte-Carlo Search
Kao Hayashi, Tomoyuki Obuchi, Yoshiyuki Kabashima
We propose a Monte-Carlo-based method for reconstructing sparse signals in the formulation of sparse linear regression in a high-dimensional setting. The basic idea of this algorit…
Empirical Bayes Method for Boltzmann Machines
Muneki Yasuda, Tomoyuki Obuchi
In this study, we consider an empirical Bayes method for Boltzmann machines and propose an algorithm for it. The empirical Bayes method allows estimation of the values of the hyper…
Cross validation in sparse linear regression with piecewise continuous nonconvex penalties and its acceleration
Tomoyuki Obuchi, Ayaka Sakata
We investigate the signal reconstruction performance of sparse linear regression in the presence of noise when piecewise continuous nonconvex penalties are used. Among such penalti…
Perfect reconstruction of sparse signals with piecewise continuous nonconvex penalties and nonconvexity control
Ayaka Sakata, Tomoyuki Obuchi
We consider compressed sensing formulated as a minimization problem of nonconvex sparse penalties, Smoothly Clipped Absolute deviation (SCAD) and Minimax Concave Penalty (MCP). The…
Semi-Analytic Resampling in Lasso
Tomoyuki Obuchi, Yoshiyuki Kabashima
An approximate method for conducting resampling in Lasso, the penalized linear regression, in a semi-analytic manner is developed, whereby the average over the resampled d…