Hi all, The LDA Vectorization patch is ready. You can take a look at: https://issues.apache.org/jira/browse/MAHOUT-683*
*Regards, Vasil* * On Thu, Apr 21, 2011 at 9:47 AM, Vasil Vasilev <[email protected]> wrote: > Ok. I am going to try out 1) suggested by Jake, then write couple of tests > and then I will file the Jira-s. > > > On Thu, Apr 21, 2011 at 8:52 AM, Grant Ingersoll <[email protected]>wrote: > >> >> On Apr 21, 2011, at 6:08 AM, Vasil Vasilev wrote: >> >> > Hi Mahouters, >> > >> > I was experimenting with the LDA clustering algorithm on the Reuters >> data >> > set and I did several enhancements, which if you find interesting I >> could >> > contribute to the project: >> > >> > 1. Created term-frequency vectors pruner: LDA uses the tf vectors and >> not >> > the tf-idf ones which result from seq2sparse. Due this fact words like >> > "and", "where", etc. get also included in the resulting topics. To >> prevent >> > that I run seq2sparse with the whole tf-idf sequence and then run the >> > "pruner". It first calculates the standard deviation of the document >> > frequencies of the words and then prunes all entries in the tf vectors >> whose >> > document frequency is bigger then 3 times the calculated standard >> deviation. >> > This ensures including most of the words population, but still pruning >> the >> > unnecessary ones. >> > >> > 2. Implemented the alpha-estimation part of the LDA algorithm as >> described >> > in the Blei, Ng, Jordan paper. This leads to better results in >> maximizing >> > the log-likelihood for the same number of iterations. Just an example - >> for >> > 20 iterations on the reuters data set the enhanced algorithm reaches >> value >> > of -6975124.693072233, compared to -7304552.275676554 with the original >> > implementation >> > >> > 3. Created LDA Vectorizer. It executes only the inference part of the >> LDA >> > algorithm based on the last LDA state and the input document vectors and >> for >> > each vector produces a vector of the gammas, that are result of the >> > inference. The idea is that the vectors produced in this way can be used >> for >> > clustering with any of the existing algorithms (like canopy, kmeans, >> etc.) >> > >> >> As Jake says, this all sounds great. Please see: >> https://cwiki.apache.org/confluence/display/MAHOUT/How+To+Contribute >> >> >
