Discussion of: Treelets--An adaptive multi-scale basis for sparse unordered data

Details Discussion of: Treelets--An adaptive multi-scale basis for sparse unordered data Discussion of: Treelets--An adaptive multi-scale basis for sparse unordered data

2008-07-25

arxiv.org/abs/0807.4023v1

Annals of Applied Statistics 2008, Vol. 2, No. 2, 484-488

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Published in at http://dx.doi.org/10.1214/08-AOAS137E the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the In

Scientific paper

10.1214/08-AOAS137E

This is a discussion of paper "Treelets--An adaptive multi-scale basis for sparse unordered data" [arXiv:0707.0481] by Ann B. Lee, Boaz Nadler and Larry Wasserman. 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.

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