@PulkitS wrote:
I was going through various matrix factorization techniques such as SVD, Non negative matrix factorization. I understood that these techniques are used to reduce the dimensions of large datasets. Non negative matrix factorization only takes positive values as input while SVD can take both positive and negative values.
While deciding which technique to use for matrix factorization, is it the only criteria that we look for, i.e. if we only have positive values then we will use non negative matrix factorization and when we have combination of both positive and negative values we will use SVD? Or do we have some other criteria as well?
What is the difference between these two techniques and when do we use SVD?
Posts: 1
Participants: 1