hi Ted, Yes. I was considering various possibilities. one of them was this. ( scale up these dimensions, for example,multiplying by a configurable factor correction.)
I really want to mix two different vectors from the same documents with different lengths and dictionaries , (perhaps some terms of dictionaries are the same). Then I will be multiplying dimension of each vector by a configurable factor correction. My question is:.. Is it better to scale up these dimensions directly in the tf-idf sequence final mix file using this correction factors OR first do scale up in each tf-vectors and then mix vectors and recalculate the tf-idf final to minimize errors or desviations in a subsequent clustering from this tf-idf final mix vectors. Thanks in advance for your help. One last note: I am bass player and 701q AKG with fiio E12+E09K is a perfect combination!! ;-) 2015-01-14 20:12 GMT+01:00 Ted Dunning <[email protected]>: > The easiest way is to scale those dimensions up. > > > > On Wed, Jan 14, 2015 at 2:41 AM, Miguel Angel Martin junquera < > [email protected]> wrote: > > > hi all, > > > > > > I am clustering using kmeans several text documents from distintct > sources > > and I have generated the sparse vectors of each document yet. > > I want to boost some dimensions in the sparse vectors. > > > > what is the best way to do this ? > > > > is it a good idea load the vectors and find the dimensions values of tf > > or tf-idf and boost this values? > > > > > > Thanks in advance and regards > > >
