期刊
JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION
卷 81, 期 2, 页码 167-178出版社
TAYLOR & FRANCIS LTD
DOI: 10.1080/00949650903222343
关键词
high-dimension low-sample-size; linear discriminant analysis
资金
- NSF
The problem of characterization of canonical vectors corresponding to unit sample canonical correlations in the non-full-rank case is considered. Classical work in this area is revisited and a new geometric characterization is developed. Applications are considered for classification problems that arise in the high-dimension low-sample-size setting. In that context, we show that Fisher's linear discriminant analysis and canonical correlation do not, in general, coincide and conduct empirical comparisons between the two methods.
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