1.1 --- a/grant.txt Thu Apr 23 03:12:01 2009 -0700 1.2 +++ b/grant.txt Fri Apr 24 01:12:36 2009 -0700 1.3 @@ -205,7 +205,7 @@ 1.4 \cite{thompson_genomic_2008} describes an analysis of the anatomy of 1.5 the hippocampus using the ABA dataset. In addition to manual analysis, 1.6 two clustering methods were employed, a modified Non-negative Matrix 1.7 -Factorization (NNMF), and a hierarchical recursive bifurcation clustering scheme based on correlation as the similarity score. The paper yielded impressive results, proving the usefulness of computational genomic anatomy. We have run NNMF on the cortical dataset 1.8 +Factorization (NNMF), and a hierarchical bifurcation clustering scheme using correlation as similarity. The paper yielded impressive results, proving the usefulness of computational genomic anatomy. We have run NNMF on the cortical dataset, and while the results are promising, other methods may perform as well or better (see Preliminary Studies, Figure \ref{dimReduc}). 1.9 1.10 %% \footnote{We ran "vanilla" NNMF, whereas the paper under discussion used a modified method. Their main modification consisted of adding a soft spatial contiguity constraint. However, on our dataset, NNMF naturally produced spatially contiguous clusters, so no additional constraint was needed. The paper under discussion also mentions that they tried a hierarchical variant of NNMF, which we have not yet tried.} and while the results are promising, they also demonstrate that NNMF is not necessarily the best dimensionality reduction method for this application (see Preliminary Studies, Figure \ref{dimReduc}). 1.11