A classifier under the strongly spiked eigenvalue model in high-dimension, low-sample-size context. Issue 7 (2nd April 2020)

• Record Type: Journal Article
• Authors: Ishii, Aki
• Is Part Of: Communications in statistics. Volume 49:Issue 7(2020)
• Page Start: 1561
• Page End: 1577
• Publication Date: 2020-04-02
• Subjects: Data transformation -- HDLSS -- Large p -- small n -- Noise-reduction methodology -- SSE model; primary 62H30 -- secondary 62H25; Mathematical statistics -- Periodicals; Mathematics; Statistics; 519.2
• DOI: 10.1080/03610926.2018.1528365
• Languages: English
• ISSNs: 0361-0926

Abstract

Abstract: We 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.