20 citations · 33 across the 10 of their papers we have counts for
10 papers
Automatic sparse PCA for high-dimensional data
Kazuyoshi Yata, Makoto Aoshima
Sparse principal component analysis (SPCA) methods have proven to efficiently analyze high-dimensional data. Among them, threshold-based SPCA (TSPCA) is computationally more cost-e…
High Dimensional Statistical Analysis and its Application to ALMA Map of NGC 253
Tsutomu T. Takeuchi, Kazuyoshi Yata, Kento Egashira +6
In astronomy, if we denote the dimension of data as and the number of samples as , we often meet a case with . Traditionally, such a situation is regarded as ill-po…
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…
Non-asymptotic results for Cornish--Fisher expansions
V. V. Ulyanov, M. Aoshima, Y. Fujikoshi
We get the computable error bounds for generalized Cornish-Fisher expansions for quantiles of statistics provided that the computable error bounds for Edgeworth-Chebyshev type expa…
Two-sample tests for high-dimension, strongly spiked eigenvalue models
Makoto Aoshima, Kazuyoshi Yata
We consider two-sample tests for high-dimensional data under two disjoint models: the strongly spiked eigenvalue (SSE) model and the non-SSE (NSSE) model. We provide a general test…
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…
High-dimensional quadratic classifiers in non-sparse settings
Makoto Aoshima, Kazuyoshi Yata
We consider high-dimensional quadratic classifiers in non-sparse settings. The target of classification rules is not Bayes error rates in the context. The classifier based on the M…