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math.ST2015
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…
math.ST2015★ 4 cited
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…