13 citations · 52 across the 18 of their papers we have counts for
18 papers
Functional Multiple-Set Canonical Correlation Analysis Revisited: From Finite-Dimensional Samples to Infinite-Dimensional Populations
Michio Yamamoto, Yoshikazu Terada
We develop a population-level formulation of functional multiple-set canonical correlation analysis (P-FMCCA) for multivariate functional data in an infinite-dimensional Hilbert sp…
Exponential mixing properties of nonlinear functional autoregressive models
Shuntarou Suzuki, Yoshikazu Terada
The importance of functional data analysis has increased substantially in recent years. In machine learning, nonlinear function regression based on deep neural networks is referred…
Tree-Guided -Convex Clustering
Bingyuan Zhang, Yoshikazu Terada
Convex clustering is a modern clustering framework that guarantees globally optimal solutions and performs comparably to other advanced clustering methods. However, obtaining a com…
Statistical properties of matrix decomposition factor analysis
Yoshikazu Terada
Numerous estimators have been proposed for factor analysis, and their statistical properties have been extensively studied. In the early 2000s, a novel matrix factorization-based a…
Sparse factor models of high dimension
Benjamin Poignard, Yoshikazu Terada
We consider the estimation of a sparse factor model where the factor loading matrix is assumed sparse. The estimation problem is reformulated as a penalized M-estimation criterion,…
Convex Clustering through MM: An Efficient Algorithm to Perform Hierarchical Clustering
Daniel J. W. Touw, Patrick J. F. Groenen, Yoshikazu Terada
Convex clustering is a modern method with both hierarchical and -means clustering characteristics. Although convex clustering can capture complex clustering structures hidden in…