2007-01-29
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Notes on Page 71 (first page of paper)

Overall it seems we should try this (DPDR) on our data.

Add to TODO list: 
    find (write) implementation for Weka, 
    Ensure SVM in Weka uses RBF kernel 
    (or something that relies only on Euclidean or cosine 
    similarity measures)

This paper deals only with feature extraction methods as a 
way to reduce the dimensionality of data. It is not about 
attribute selection.

Notes on Page 72

The authors refer to "powerful" classifiers such as SVMs but 
do not define "powerful".

Notes on Page 73

The data used in this study is largely similar to our own 
pancreatic patient data. Both have very few instance and a 
large number of attributes. In fact, the data used in this 
study has far more attribute per instance than our own.
One thing to note when viewing the results is that these data 
sets deal with gene expression levels, that are known to be 
string indicating factors in the target attribute. I am 
currently not as certain our data is complete with respect to 
capturing all the factors determining our target attributes.

One interesting point is that for all the feature extraction 
methods used, the target dimensionality is a parameter with the 
exception of DPDR. All have the number of instances as one of 
the values tried, with the exception of MDS, even though it 
certainly looks possible. I wonder why the authors chose to not 
have a common point of reference. I did not see mention of
specifying the number of dimensions DPDR results in being used 
as a value for this parameter.

Notes on Page 74

The results shown in Table 3 are quite good. See previous note 
on confidence of our own pancreatic patient data.