  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.
