We introduce a new method based on nonnegative matrix factorization, Neural NMF, for detecting latent hierarchical structure in data. Datasets with hierarc
>>>nmf=Nmf(common_corpus,num_topics=50,kappa=0.1,eval_every=5)# decrease training step size The NMF should be used whenever one needs extremely fast and memory optimized topic model. classgensim.models.nmf.Nmf(corpus=None,num_topics=100,id2word=None,chunksize=2000,passes=1,kappa=1.0,minimu...
The critical need to include unshared features in single-cell integration analyses motivated us to extend our previous approach. We developed UINMF, a nonnegative matrix factorization algorithm that allows the inclusion of both shared and unshared features. UINMF can integrate data matrices with neit...