We introduce the localized Lasso, which learns models that both are interpretable and have
a high predictive power in problems with high dimensionality d and small sample size n …

Understanding spatio-temporal activities in a city is a typical problem of spatio-temporal data
analysis. For this analysis, tensor factorization methods have been widely applied for …

Degrees of freedom is a covariance penalty related to penalized model selection
procedures such as Mallows' C p and AIC. We study the degrees of freedom of two …

We introduce the sparse network lasso, which is suited for interpreting models in addition to
having high predicting power, for high dimensionality d and small sample size n types of …

The proximal problem for structured penalties obtained via convex relaxations of
submodular functions is known to be equivalent to minimizing separable convex functions …

A widely accepted principle in statistical learning is that a good estimator is obtained through
a good control of model complexity. Typically, low-complexity models are obtained as low …

Various regularizers inducing structured-sparsity are constructed as Lovász extensions of
submodular functions. In this paper, we consider a hierarchical probabilistic model of linear …

We observe various kinds of interesting events occurring in our daily lives, and try to
represent these events in a simple manner. A relational model is one of the de facto systems …

高階結合**則化による時空間変化パターン検出 Page 1 高階結合**則化による時空間変化パターン
検出 Structured regularizer for spatio-temporal matrix completion 竹内孝 1∗ 河原吉伸 2 岩田具 …

本論文は, 実世界で現れる異種混合的あるいは時空間的なつながりをもつ複雑な関係データを解析
し, 高精度での予測や有用な知見を得るための, テンソル分解にもとづく機械学習法についての研究 …