Effective PCA for high-dimension, low-sample-size data with singular value decomposition of cross data matrix. - Kazuyoshi Yata; Makoto Aoshima (J. Multivar. Anal., 2010)

Effective PCA for high-dimension, low-sample-size data with noise reduction via geometric representations. - Kazuyoshi Yata; Makoto Aoshima (J. Multivar. Anal., 2012)

Correlation tests for high-dimensional data using extended cross-data-matrix methodology. - Kazuyoshi Yata; Makoto Aoshima (J. Multivar. Anal., 2013)

PCA consistency for the power spiked model in high-dimensional settings. - Kazuyoshi Yata; Makoto Aoshima (J. Multivar. Anal., 2013)

High-dimensional inference on covariance structures via the extended cross-data-matrix methodology. - Kazuyoshi Yata; Makoto Aoshima (J. Multivar. Anal., 2016)

Support vector machine and its bias correction in high-dimension, low-sample-size settings. - Yugo Nakayama; Kazuyoshi Yata; Makoto Aoshima (CoRR, 2017)

Clustering by principal component analysis with Gaussian kernel in high-dimension, low-sample-size settings. - Yugo Nakayama; Kazuyoshi Yata; Makoto Aoshima (J. Multivar. Anal., 2021)

Consistency of the objective general index in high-dimensional settings. - Takuma Bando; Tomonari Sei; Kazuyoshi Yata (J. Multivar. Anal., 2022)

Geometric classifiers for high-dimensional noisy data. - Aki Ishii; Kazuyoshi Yata; Makoto Aoshima (J. Multivar. Anal., 2022)

Proposing a Low-Rank Approximation Method with Mathematical Guarantees for High-Dimensional Tensor Data. - Hiroki Hasegawa; Kazuyoshi Yata; Yukihiko Okada; Jun Kunimatsu (IEEE Big Data, 2024)

Asymptotic properties of hierarchical clustering in high-dimensional settings. - Kento Egashira; Kazuyoshi Yata; Makoto Aoshima (J. Multivar. Anal., 2024)