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Discussion of: Treelets – an adaptive multi-scale basis for sparse unordered data. (English) Zbl 1400.62011

Summary: This is a discussion of paper “Treelets-An adaptive multi-scale basis for sparse unordered data” by Ann B. Lee, Boaz Nadler and Larry Wasserman [A. B. Lee et al., Ann. Appl. Stat. 2, No. 2, 435–471 (2008; Zbl 1400.62274)]. In this paper the authors defined a new type of dimension reduction algorithm, namely, the treelet algorithm. The treelet method has the merit of being completely data driven, and its decomposition is easier to interpret as compared to PCR. It is suitable in some certain situations, but it also has its own limitations. I will discuss both the strength and the weakness of this method when applied to microarray data analysis.

MSC:

62-07 Data analysis (statistics) (MSC2010)
62P10 Applications of statistics to biology and medical sciences; meta analysis
62H30 Classification and discrimination; cluster analysis (statistical aspects)

Citations:

Zbl 1400.62274
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References:

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