DMelt:Numeric/7 PCA Analysis
Principal Component Analysis (PCA) is an important for many applications. Read : Principal component analysis .
Below we will show examples of Principal Component Analysis (PCA) data transformation using matrices as input. We will consider a situation when some of the columns in the data matrix are linearly dependent or when there are more columns than rows in the data matrix i.e. there are more dimensions than samples in the data set. In the above example we train the data and then apply to a test data.
|No access to this part. Use Member area to request membership. If you are already a member, login to Member area and come back to this wiki.|
The output of this code is shown below:
-0.9999999999999998, -0.5773502691896268 -0.08571428571428596, 1.732050807568878