Kazuyoshi Yata

According to our database, Kazuyoshi Yata authored at least 13 papers between 2010 and 2026.

Collaborative distances:

• Dijkstra number of six.
• Erdős number of five.

2026

Automatic sparse estimation of the high-dimensional cross-covariance matrix.

Tetsuya Umino, Kazuyoshi Yata, Makoto Aoshima

J. Multivar. Anal., 2026

2025

Asymptotic Properties of Kernel K-means and Its Comparison with Conventional K-means in High-Dimensional Settings.

Kento Egashira, Kazuyoshi Yata, Makoto Aoshima

Proceedings of the Knowledge-Based and Intelligent Information & Engineering Systems: Proceedings of the 29th International Conference KES-2025, 2025

2024

Asymptotic properties of hierarchical clustering in high-dimensional settings.

Kento Egashira, Kazuyoshi Yata, Makoto Aoshima

J. Multivar. Anal., 2024

Proposing a Low-Rank Approximation Method with Mathematical Guarantees for High-Dimensional Tensor Data.

Hiroki Hasegawa, Kazuyoshi Yata, Yukihiko Okada, Jun Kunimatsu

Proceedings of the IEEE International Conference on Big Data, 2024

2022

Geometric classifiers for high-dimensional noisy data.

Aki Ishii, Kazuyoshi Yata, Makoto Aoshima

J. Multivar. Anal., 2022

Consistency of the objective general index in high-dimensional settings.

Takuma Bando, Tomonari Sei, Kazuyoshi Yata

J. Multivar. Anal., 2022

2021

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

2017

Support vector machine and its bias correction in high-dimension, low-sample-size settings.

Yugo Nakayama, Kazuyoshi Yata, Makoto Aoshima

CoRR, 2017

2016

High-dimensional inference on covariance structures via the extended cross-data-matrix methodology.

Kazuyoshi Yata, Makoto Aoshima

J. Multivar. Anal., 2016

2013

PCA consistency for the power spiked model in high-dimensional settings.

Kazuyoshi Yata, Makoto Aoshima

J. Multivar. Anal., 2013

Correlation tests for high-dimensional data using extended cross-data-matrix methodology.

Kazuyoshi Yata, Makoto Aoshima

J. Multivar. Anal., 2013

2012

Effective PCA for high-dimension, low-sample-size data with noise reduction via geometric representations.

Kazuyoshi Yata, Makoto Aoshima

J. Multivar. Anal., 2012

2010

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