I wonder why number of components in H2o PCA algorithm is limited to 9. It is not sure sometimes to be enough.
k: Specify the rank of matrix approximation. This can be a value from 1 to 9 and defaults to 1.
$\begingroup$
$\endgroup$
3
-
$\begingroup$ Many times, when doing PCA, one would want to break data down into a few interpret-able factors, meaning researchers would only consider the first ~3-4 PCs in their analysis. This is the approach general when using PCA for exploratory factors analysis. $\endgroup$– ERTCommented Jul 30, 2018 at 11:36
-
1$\begingroup$ If you use PCA for visualization might be you are right . But if I want to transform a 100 features to some another space of features and hold significant amount of variance I'd try some larger values $\endgroup$– NiMaCommented Jul 31, 2018 at 16:42
-
$\begingroup$ It would be nice if you specified your R packages and functions you are using $\endgroup$– ERTCommented Jul 31, 2018 at 17:00
Add a comment
|
1 Answer
$\begingroup$
$\endgroup$
1
The H2O prcomp algorithm (i.e. PCA) is not limited to 10 components. The limit is the number of columns in your data (or the number of rows if that is smaller).
The online documentation (http://docs.h2o.ai/h2o/latest-stable/h2o-docs/data-science/pca.html and http://docs.h2o.ai/h2o/latest-stable/h2o-docs/data-science/algo-params/k.html) is wrong. (I'll ping someone at H2O.)
Here is quick R script to show this:
library(h2o)
h2o.init()
d <- as.h2o(matrix(runif(2000),nrow = 40, ncol = 50))
m = h2o.prcomp(d, k = 20)
m
I.e. make a matrix of random data, and request the first 20 components.
The output looks like:
Importance of components:
pc1 pc2 pc3 pc4 pc5 pc6 pc7 pc8 pc9 pc10 pc11
Standard deviation 3.569561 0.599236 0.560946 0.529781 0.518060 0.495266 0.477426 0.438890 0.429845 0.417752 0.407795
Proportion of Variance 0.754456 0.021262 0.018631 0.016619 0.015892 0.014524 0.013496 0.011406 0.010940 0.010333 0.009847
Cumulative Proportion 0.754456 0.775718 0.794349 0.810968 0.826859 0.841383 0.854880 0.866285 0.877225 0.887559 0.897405
pc12 pc13 pc14 pc15 pc16 pc17 pc18 pc19 pc20
Standard deviation 0.394316 0.386367 0.370733 0.362824 0.354425 0.343219 0.321725 0.304999 0.295844
Proportion of Variance 0.009206 0.008839 0.008138 0.007795 0.007438 0.006975 0.006129 0.005508 0.005182
Cumulative Proportion 0.906612 0.915451 0.923589 0.931384 0.938822 0.945797 0.951926 0.957434 0.962616
Note that only 96.2% of the variance is explained by the first 20 components. If you set k to 40 you will see 100% of the variance explained. If you set k higher than 40 you will get an error message.
Note: the python API will behave identically.
Aside: I needed so many components because this was random data. With data that has more signal it should not need so many to capture most of the variance. But for high-dimensional data, using more than 10 components is a very reasonable thing to do.
answered Jul 31, 2018 at 19:56
-
1$\begingroup$ That is I was think of it . Actually , I could check it by myself . That k is actually not limited. Thanks a lot , Darren. $\endgroup$– NiMaCommented Aug 2, 2018 at 15:58