Input to tfidf Matrix with a list of text documents, output is a matrix which contains a numerical value for each (word, document) pair. How can I use that matrix to perform feature selection, i.e. reduce the size of the features where matrix are very sparse ... 2000+ unique words (terms)?
so i would as about the following ideas:
can i considers Tf-Idf threshold such that if tf-idf(word) vector across all documents > threshold then ok, otherwise remove, if this idea is valid ... can i select threshold based on (mean and average value in every word vector) can some one give me suggestion about this?
i have read so many text mining books, they mention about using tf,idf and tf-idf as a suitable algorithms for selection subset of features from unlabeled documents so can some one give an ideas about using these methods ?
finally, can i used NMF(non-matrix factorization or SVD) for optimal number of features then using optimal features..
thanks for any suggestion.