I am looking to do classification on my text data. I have
300 classes, 200 training documents per class (so
60000 documents in total) and this is likely to result in very high dimensional data (we may be looking in excess of 1million dimensions).
I would like to perform the following steps in the pipeline (just to give you a sense of what my requirements are):
- Converting each document to feature vector (
vector space model)
Mutual Informationbased preferably, or any other standard ones)
- Training the classifier (
- Predicting unseen data based on the classifier model trained.
So the question is what tools/framework do I use for handling such high dimensional data? I am aware of the usual suspects (R, WEKA...) but as far as my knowledge goes (I may be wrong) possibly none of them can handle data this large. Is there any other off the shelf tool that I could look at?
If I have to parallelize it, should I be looking at Apache Mahout? Looks like it may not quite yet provide the functionality I require.
Thanks to all in advance.
Update: I looked around this website, R mailing list and the internet in general. It appears to me that the following problems could emerge in my situation:
(2) Since I will need to use an ensemble of R packages (pre-processing, sparse matrices, classifiers etc.) interoperability between the packages could become a problem, and I may incur an additional overhead in converting data from one format to another. For example, if I do my pre-processing using
tm (or an external tool like WEKA) I will need to figure out a way to convert this data into a form that the HPC libraries in R can read. And again it is not clear to me if the classifier packages would directly take in the data as provided by the HPC libraries.
Am I on the right track? And more importantly, am I making sense ?