Nonnegative Matrix Factorization

[jhmarcus]

Hey @jacobcvt12,

This is post is relevant to you …

Loss increasing in inference of Poisson Factorization

I am try to implement a simple Poisson Factorization theta ~ Gamma(a, b) beta ~ Gamma(c, d) x ~ Poisson( theta . beta ) My codes: from edward.models import Poisson, Gamma import edward as ed # Toy dataset M = (np.random.random((300,200))>0.9).astype(int) # Model theta = Gamma(1.*tf.ones([300, 10]), 1.) beta = Gamma(1.*tf.ones([200, 10]), 1.) X = Poisson(tf.matmul(theta, tf.transpose(beta))) # Inference q_theta = Gamma(tf.nn.softplus(tf.Variable(tf.random_normal([300, 10]))), …

maybe you could also try using a lognormal variational distributions for U and V?