An H2OFrame object containing the
variables in the model.
(Optional) A vector containing the data columns on which SVD operates.
The number of right singular vectors to be computed. This must be
between 1 and min(ncol(training_frame), nrow(training_frame)) inclusive.
(Optional) The unique hex key assigned to the
resulting model. Automatically generated if none is provided.
The maximum number of iterations to run each power
iteration loop. Must be between 1 and 1e6 inclusive.
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 each
column; "STANDARDIZE" for demeaning and descaling; and "NORMALIZE"
for demeaning and dividing each column by its range (max - min).
A character string that indicates how SVD should be calculated.
Possible values are "GramSVD": distributed computation of the Gram matrix
followed by a local SVD using the JAMA package, "Power": computation of
the SVD using the power iteration method, "Randomized": approximate SVD
by projecting onto a random subspace (see references).
(Optional) Random seed used to initialize the right singular vectors
at the beginning of each power method iteration.
(Optional) A logical value indicating whether all
factor levels should be included in each categorical column expansion.
If FALSE, the indicator column corresponding to the first factor level
of every categorical variable will be dropped. Defaults to TRUE.
Maximum allowed runtime in seconds for model training. Use 0 to disable.
Returns an object of class H2ODimReductionModel.
N. Halko, P.G. Martinsson, J.A. Tropp. Finding structure with randomness: Probabilistic algorithms for constructing approximate matrix decompositions[http://arxiv.org/abs/0909.4061]. SIAM Rev., Survey and Review section, Vol. 53, num. 2, pp. 217-288, June 2011.