Makoto Aoshima

12 papers B 1Journal 11

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Automatic sparse estimation of the high-dimensional cross-covariance matrix. J. Multivar. Anal. Tetsuya Umino, Kazuyoshi Yata, Makoto Aoshima
2025	B	`conf`	Asymptotic Properties of Kernel K-means and Its Comparison with Conventional K-means in High-Dimensional Settings. KES Kento Egashira, Kazuyoshi Yata, Makoto Aoshima
2024	J	`jnl`	Asymptotic properties of hierarchical clustering in high-dimensional settings. J. Multivar. Anal. Kento Egashira, Kazuyoshi Yata, Makoto Aoshima
2022	J	`jnl`	Geometric classifiers for high-dimensional noisy data. J. Multivar. Anal. Aki Ishii, Kazuyoshi Yata, Makoto Aoshima
2021	J	`jnl`	Clustering by principal component analysis with Gaussian kernel in high-dimension, low-sample-size settings. J. Multivar. Anal. Yugo Nakayama, Kazuyoshi Yata, Makoto Aoshima
2017	J	`jnl`	Support vector machine and its bias correction in high-dimension, low-sample-size settings. CoRR Yugo Nakayama, Kazuyoshi Yata, Makoto Aoshima
2016	J	`jnl`	High-dimensional inference on covariance structures via the extended cross-data-matrix methodology. J. Multivar. Anal. Kazuyoshi Yata, Makoto Aoshima
2013	J	`jnl`	Correlation tests for high-dimensional data using extended cross-data-matrix methodology. J. Multivar. Anal. Kazuyoshi Yata, Makoto Aoshima
2013	J	`jnl`	PCA consistency for the power spiked model in high-dimensional settings. J. Multivar. Anal. Kazuyoshi Yata, Makoto Aoshima
2012	J	`jnl`	Effective PCA for high-dimension, low-sample-size data with noise reduction via geometric representations. J. Multivar. Anal. Kazuyoshi Yata, Makoto Aoshima
2010	J	`jnl`	Effective PCA for high-dimension, low-sample-size data with singular value decomposition of cross data matrix. J. Multivar. Anal. Kazuyoshi Yata, Makoto Aoshima
2009	J	`jnl`	Optimal discriminant functions for normal populations. J. Multivar. Anal. Hirofumi Wakaki, Makoto Aoshima