KernelPCA

The KernelPCA algorithm uses the scikit-learn KernelPCA to reduce the number of fields by extracting uncorrelated new features out of data. The difference between KernelPCA and PCA is the use of kernels in the former, which helps with finding nonlinear dependencies among the fields. Currently, KernelPCA only supports the Radial Basis Function (rbf) kernel.

For descriptions of the gamma, degree, tolerance, and max_iteration parameters, see the scikit-learn documentation at http://scikit-learn.org/stable/modules/generated/sklearn.decomposition.KernelPCA.html.

Note: Kernel-based methods such as KernelPCA tend to work best when the data is scaled, for example, using our StandardScaler algorithm: into kpca_model. For details, see ''A Practical Guide to Support Vector Classification'' at https://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf.

Parameters

The k parameter specifies the number of features to be extracted from the data. The other parameters are for fine tuning of the kernel.

Syntax

fit KernelPCA <fields> [into <model name>]
[degree=<int>] [k=<int>] [gamma=<int>]
[tolerance=<int>] [max_iteration=<int>]

You can save KernelPCA models using the into keyword and apply new data later using the apply command.

... | apply user_feedback_model

Syntax constraints

You cannot inspect the model learned by KernelPCA with the summary command.

Example

The following example uses KernelPCA on a test set.

... | fit KernelPCA * k=3 gamma=0.001 | ...

Local availability Permalink to this section#

• Local class: KernelPCA
• Source file: Splunk_ML_Toolkit/bin/algos/KernelPCA.py (in-repo path Splunk_ML_Toolkit/bin/algos/KernelPCA.py)
• algos.conf stanza: [KernelPCA]
• Class bases: PCAAlgo

Source Permalink to this section#

Adapted from the Splunk AI Toolkit 5.6.4 documentation at /en/splunk-cloud-platform/apply-machine-learning/use-ai-toolkit/5.6.4/algorithms-and-scoring-metrics-in-the-ai-toolkit/algorithms-in-the-ai-toolkit (section: preprocessor).