Database Reference
In-Depth Information
$
cmake ..
$
make
This creates a binary executable named
tapkee
.
Linear and Nonlinear Mappings
First, we'll scale the features using standardization such that each feature is equally
important. This generally leads to better results when applying machine-learning
algorithms.
To scale we use a combination of
cols
and
Rio
:
$
< wine-both.csv cols -C
type
Rio -f scale > wine-both-scaled.csv
Now we apply both dimensionality reduction techniques and visualize the mapping
using
Rio-scatter
(
Figure 9-3
):
$
< wine-both-scaled.csv cols -C
type
,quality body tapkee --method pca |
>
header -r x,y,type,quality | Rio-scatter x y
type
| display
Figure 9-3. Linear dimensionality reduction with PCA
$
< wine-both-scaled.csv cols -C
type
,quality body tapkee --method t-sne |
>
header -r x,y,type,quality | Rio-scatter x y
type
| display
