A classifier under the strongly spiked eigenvalue model in high-dimension,low-sample-size context |
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Authors: | Aki Ishii |
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Affiliation: | 1. Department of Information Sciences, Tokyo University of Science, Chiba, Japana.ishii@rs.tus.ac.jp |
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Abstract: | AbstractWe consider the classification of high-dimensional data under the strongly spiked eigenvalue (SSE) model. We create a new classification procedure on the basis of the high-dimensional eigenstructure in high-dimension, low-sample-size context. We propose a distance-based classification procedure by using a data transformation. We also prove that our proposed classification procedure has consistency property for misclassification rates. We discuss performances of our classification procedure in simulations and real data analyses using microarray data sets. |
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Keywords: | Data transformation HDLSS Large p small n Noise-reduction methodology SSE model |
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