[Topic-models] The role of the parameters in hlda-c

Firman, Michael michael.firman.10 at ucl.ac.uk
Fri Mar 4 08:32:54 EST 2011

Dear all,

I have some questions about the role of the parameters used in the settings file of Blei's implementation of hlda. This is my current knowledge (and lack of it!):

DEPTH 4 			
// The number of levels in the tree being learned

ETA 1.0 0.5 0.25 0.125
// I am not entirely sure, but I suspect this to be a prior on the distribution of words on a path through the tree. In this instance, for example, more words would be expected to be assigned to the root than to all the other nodes in the tree combined.

GAM 0.5 0.5 0.5
// This is the gamma used in the CRP; one value of gamma for each level in the tree. As I understand it, a small gamma for a level encourages fewer new branches at each node, while a large gamma encourages many branches (roughly speaking).

// These final 6 parameters I am more unsure about. By empirically adjusting the SCALING_* values, my tree seemed to become 'noisier' and 'cleaner', but it was hard to determine the exact effect they were having.

Does anyone have any further knowledge of these parameters, or a reference they could point me to? In my application, I have a strong prior on the shape of tree I expect to see (e.g. a roughly symmetric tree, with ~4 nodes on level 1, and the nodes on level 2 evenly distributed between level 1 nodes), but I am unsure if I can translate these into the settings.txt options.



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