Regularized Latent Semantic Indexing in R - r

Question

I am trying to implement the Regularized Latent Semantic Indexing (RLSI) algorithm on R.
The original paper can be found here:
http://research.microsoft.com/en-us/people/hangli/sigirfp372-wang.pdf
Below is my code.
Here, I generate a matrix D from two matrices U and V. Each column of U correspond to a topic vector, and it is made to be sparse. After that, I apply RLSI on the D matrix to see if I can factorize it into two matrices, one of which has sparse vectors like U.
However, the resulting U is far from being sparse. Actually, every element of it is filled with numbers.
Is there something wrong with my code?
Thank you very much in advance.
library(magrittr)
# functions
updateU <- function(D,U,V){
S <- V %*% t(V)
R <- D %*% t(V)
for(m in 1:M){
u_m <- rep(0, K)
u_previous <- u_m
diff_u <- 100
while(diff_u > 0.1){
for(k in 1:K){
w_mk <- R[m,k] - S[k,-k] %*% U[m,-k]
in_hinge <- (abs(w_mk) - 0.5 * lambda_1)
u_m[k] <- (ifelse(in_hinge > 0, in_hinge, 0) * ifelse(w_mk >= 0, 1, -1)) / S[k,k]
}
diff_u <- sum(u_m - u_previous)
u_previous <- u_m
}
U[m,] <- u_m
}
return(U)
}
updateV <- function(D,U,V){
Sigma <- solve(t(U) %*% U + lambda_2 * diag(K))
Phi <- t(U) %*% D
V <- Sigma %*% Phi
return(V)
}
# Set constants
M <- 5000
N <- 1000
K <- 30
lambda_1 <- 1
lambda_2 <- 0.5
# Create D
originalU <- c(rpois(50000, lambda = 10), rep(0, 100000)) %>% sample(., 150000) %>% matrix(., M, K)
originalV <- rpois(30000, lambda = 5) %>% sample(., 30000) %>% matrix(., K, N)
D <- originalU %*% originalV
# Initialize U and V
V <- matrix(rpois(30000, lambda = 5), K, N)
U <- matrix(0, M, K)
# Run RLSI (iterate 100 times for now)
for(t in 1:100){
cat(t,":")
U <- updateU(D,U,V)
V <- updateV(D,U,V)
loss <- sum((D - U %*% V) ^ 2)
cat(loss, "\n")
}

Answer 1

I've got it. Each row in U has to be set to a zero vector each time updateU function is run.