Hi Sjors and all,
I am trying to get a reasonable number of 2D class averages by
running a 2D-classification on an old icosahedral virus dataset (about
10,000 images, 50 classes). After each run I select the classes with
features recognizable as viral, remove the trash, and run it again. I am
now at run 5 and I am still getting only 2 classes recognizable as viral
projections (in sum about 99% of the images) and about 40 classes
containing between 1 and 20 'bad' images. I have tried T=1, 1.5, and 2
and angular sampling 5 and 2.5 but regardless of these parameters it
seems to go on for ever (each new run finds new 'trash images' and I do
not get more than the 2 main classes (with the second one at about 3 %
only)). I admit that the dataset is skewed towards one single view but
shouldn't I get more than two main class averages ? Is there a way of
removing the trash and getting a reasonable number of views once for ever?
Thanks for hints, Dieter
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Dieter Blaas,
Max F. Perutz Laboratories
Medical University of Vienna,
Inst. Med. Biochem., Vienna Biocenter (VBC),
Dr. Bohr Gasse 9/3,
A-1030 Vienna, Austria,
Tel: 0043 1 4277 61630,
Fax: 0043 1 4277 9616,
e-mail: [log in to unmask]
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