The title doesn't make sense but allow me to explain. I have a set of gene expression data with over 10000 genes as features. There are roughly 30 samples, and let's say there are 10 from class A, 10 from class B and 10 others. In the "others" samples, each of them has a potentially distinct class, but may also be in class A, B or a (few) new class(es) C(,D,E,etc.). My hypothesis is that if I can select a set of feature for A, B and each of the samples in "others", then do a clustering such as DBSCAN/OPTICS, I can reveal the structure of the data set, putting the "others" into A, B, some new classes, or an outlier that doesn't belong to any class.
I have tried information gain in FSelector in R with two different filtering methods: (1) doing two separate information gain calculation for class A vs B+others and class B vs A+others; (2) doing one information gain calculation by labelling the samples A, B and as individual classes for all samples in "others" i.e. each class label for each "others" sample contains one and only one sample.
Which one is the correct way/better way? And is there any good suggestions for this type of feature selection with single cases in multiple classes e.g. svm,lasso?