Description Usage Arguments Value References Examples
Optimize the weighted classification loss given a weight tensor, an observed data tensor, and a large margin loss.
1 |
Ybar |
A given (possibly noisy and incomplete) data tensor. |
W |
A weight tensor used in the weighted classification loss. |
r |
Tensor rank to be fitted. |
type |
A large margin loss to be used. Logistic or hinge loss is available. |
start |
Choice of initialization method. Use random initialization if |
The returned object is a list of components.
binary_obj
- Trajectory of binary loss values over iterations.
obj
- Trajectory of weighted classification loss values over iterations.
iter
- The number of iterations.
error
- Trajectory of errors over iterations.
fitted
- A tensor that optimizes the weighted classification loss.
Lee, C., & Wang, M. (2021). Beyond the Signs: Nonparametric Tensor Completion via Sign Series. arXiv preprint arXiv:2102.00384.
1 2 3 4 5 6 7 8 9 10 11 | library(rTensor)
indices = c(2,3,4)
noise = rand_tensor(indices)@data
Theta = array(runif(prod(indices),min=-3,max = 3),indices)
# The signal plus noise model
Y = Theta + noise
# Optimize the weighted classification for given a sign tensor sign(Y) and a weight tensor abs(Y)
result = Alt(sign(Y),abs(Y),r = 3,type = "logistic",start = "linear")
signTheta = sign(result$fitted)
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