Multidimensional scaling with the nested hypersphere model for percentile dissimilarities. - Yoshikazu Terada; Hiroshi Yadohisa (Complex Adaptive Systems, 2011)

Clustering for high-dimension, low-sample size data using distance vectors. - Yoshikazu Terada (CoRR, 2013)

Local Ordinal Embedding. - Yoshikazu Terada; Ulrike von Luxburg (ICML, 2014)

Functional factorial K-means analysis. - Michio Yamamoto; Yoshikazu Terada (Comput. Stat. Data Anal., 2014)

Kernel Normalized Cut: a Theoretical Revisit. - Yoshikazu Terada; Michio Yamamoto (ICML, 2019)

More Powerful Selective Kernel Tests for Feature Selection. - Jen Ning Lim; Makoto Yamada; Wittawat Jitkrittum; Yoshikazu Terada; Shigeyuki Matsui; Hidetoshi Shimodaira (AISTATS, 2020)

Fast generalization error bound of deep learning without scale invariance of activation functions. - Yoshikazu Terada; Ryoma Hirose (Neural Networks, 2020)

Dynamic visualization for L1 fusion convex clustering in near-linear time. - Bingyuan Zhang; Jie Chen; Yoshikazu Terada (UAI, 2021)

Convex Clustering through MM: An Efficient Algorithm to Perform Hierarchical Clustering. - Daniel J. W. Touw; Patrick J. F. Groenen; Yoshikazu Terada (CoRR, 2022)

Sparse kernel k-means for high-dimensional data. - Xin Guan; Yoshikazu Terada (Pattern Recognit., 2023)

Enhancing the rationale of convolutional neural networks for glitch classification in gravitational wave detectors: a visual explanation. - Naoki Koyama; Yusuke Sakai 011; Seiya Sasaoka; Diego Dominguez; Kentaro Somiya; Yuto Omae; Yoshikazu Terada; Marco Meyer-Conde; Hirotaka Takahashi (Mach. Learn. Sci. Technol., 2024)

A note on the $k$-means clustering for missing data. - Yoshikazu Terada; Xin Guan (Trans. Mach. Learn. Res., 2025)

Tree-Guided L1-Convex Clustering. - Bingyuan Zhang; Yoshikazu Terada (CoRR, 2025)