4 citations · 6 across the 5 of their papers we have counts for
5 papers
Distance-based classifier by data transformation for high-dimension, strongly spiked eigenvalue models
Makoto Aoshima, Kazuyoshi Yata
We consider classifiers for high-dimensional data under the strongly spiked eigenvalue (SSE) model. We first show that high-dimensional data often have the SSE model. We consider a…
Support vector machine and its bias correction in high-dimension, low-sample-size settings
Yugo Nakayama, Kazuyoshi Yata, Makoto Aoshima
In this paper, we consider asymptotic properties of the support vector machine (SVM) in high-dimension, low-sample-size (HDLSS) settings. We show that the hard-margin linear SVM ho…
Asymptotic properties of the first principal component and equality tests of covariance matrices in high-dimension, low-sample-size context
Aki Ishii, Kazuyoshi Yata, Makoto Aoshima
A common feature of high-dimensional data is that the data dimension is high, however, the sample size is relatively low. We call such data HDLSS data. In this paper, we study asym…
High-dimensional inference on covariance structures via the extended cross-data-matrix methodology
Kazuyoshi Yata, Makoto Aoshima
In this paper, we consider testing the correlation coefficient matrix between two subsets of high-dimensional variables. We produce a test statistic by using the extended cross-dat…
Principal component analysis based clustering for high-dimension, low-sample-size data
Kazuyoshi Yata, Makoto Aoshima
In this paper, we consider clustering based on principal component analysis (PCA) for high-dimension, low-sample-size (HDLSS) data. We give theoretical reasons why PCA is effective…