Usage
h2o.glrm(training_frame, x, k, destination_key, loading_key,
transform = c("NONE", "DEMEAN", "DESCALE", "STANDARDIZE", "NORMALIZE"),
loss = c("L2", "L1", "Huber", "Poisson", "Hinge", "Logistic"),
regularization_x = c("L2", "L1"), regularization_y = c("L2", "L1"),
gamma_x = 0, gamma_y = 0, max_iterations = 1000, init_step_size = 1,
min_step_size = 0.001, init = c("Random", "PlusPlus", "SVD"),
recover_pca = FALSE, seed)Arguments
training_frame
An H2OFrame object containing the
variables in the model.
x
(Optional) A vector containing the data columns on
which k-means operates.
k
The rank of the resulting decomposition. This must be
between 1 and the number of columns in the training frame, inclusive.
destination_key
(Optional) The unique hex key assigned to the
resulting model. Automatically generated if none is provided.
loading_key
(Optional) The unique hex key assigned to the loading matrix X
in the XY decomposition. Automatically generated if none is provided.
transform
A character string that indicates how the training data
should be transformed before running PCA. Possible values are "NONE":
for no transformation, "DEMEAN": for subtracting the mean of each
column, "DESCALE": for dividing by the standard deviation of ea
loss
A character string indicating the loss function. Possible values are
"L2", "L1", "Huber", "Poisson", "Hinge" and "Logistic".
regularization_x
A character string indicating the regularization function for
the X matrix. Possible values are "L2" and "L1".
regularization_y
A character string indicating the regularization function for
the Y matrix. Possible values are "L2" and "L1".
gamma_x
The weight on the X matrix regularization term. For no X regularization,
set this value to zero.
gamma_y
The weight on the Y matrix regularization term. For no Y regularization,
set this value to zero.
max_iterations
The maximum number of iterations to run the optimization loop.
Each iteration consists of an update of the X matrix, followed by an update
of the Y matrix.
init_step_size
Initial step size. Divided by number of columns in the training
frame when calculating the proximal gradient update. The algorithm begins at
init_step_size and decreases the step size at each iteration until a
termination condition is reached.
min_step_size
Minimum step size upon which the algorithm is terminated.
init
A character string indicating how to select the initial Y matrix.
Possible values are "Random": for initialization to a random array from the
standard normal distribution, "PlusPlus": for initialization using the clusters
from k-means++ initialization, or
recover_pca
A logical value indicating whether the principal components should
be recovered during post-processing of the generalized low rank decomposition.
seed
(Optional) Random seed used to initialize the X and Y matrices.