This example shows how to order topics according to co-usage in documents This will an ordering over topics where topics that co-appear often with other topics in documents to have similar indices.

This examples uses the OrderTopics function. In this procedure, for each topic, the probability distribution over documents is calculated. For each topic pair (i,j), the (symmetrized) KL distance between topic distributions i and j is calculated. The KL distances are then subjected multidimensional scaling (MDS). The first dimension of the MDS solution is then used to get an ordering for topics.

load a document-topic count matrix saved for the nips dataset

load 'ldasingle_nips'; load 'words_nips';

extract the topics in a cell array of strings

[S]=WriteTopics( WP,BETA,WO,5,0.6 );

order these topics using the OrderTopics function

[ Order ] = OrderTopics( DP,ALPHA );

and show the resulting ordering

S( Order )

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