Kernel Principal Component Analysis
GPTKB entity
Statements (31)
| Predicate | Object |
|---|---|
| gptkbp:instanceOf |
Dimensionality reduction technique
|
| gptkbp:abbreviation |
gptkb:Kernel_PCA
|
| gptkbp:application |
Data visualization
Feature extraction Preprocessing for classification Preprocessing for clustering |
| gptkbp:basedOn |
Principal Component Analysis
|
| gptkbp:category |
Unsupervised learning
Nonlinear dimensionality reduction |
| gptkbp:commonKernels |
Gaussian kernel
Polynomial kernel Sigmoid kernel |
| gptkbp:field |
Statistics
Machine learning Pattern recognition |
| gptkbp:handles |
Nonlinear data
|
| https://www.w3.org/2000/01/rdf-schema#label |
Kernel Principal Component Analysis
|
| gptkbp:implementedIn |
gptkb:scikit-learn
R (kernlab package) |
| gptkbp:introduced |
gptkb:Bernhard_Schölkopf
|
| gptkbp:introducedIn |
1997
|
| gptkbp:limitation |
Choice of kernel affects results
Computationally intensive for large datasets |
| gptkbp:output |
Principal components in feature space
|
| gptkbp:relatedTo |
gptkb:Support_Vector_Machine
Manifold learning Linear PCA |
| gptkbp:requires |
Kernel function
|
| gptkbp:uses |
Kernel trick
|
| gptkbp:bfsParent |
gptkb:Gaussian_Kernel
|
| gptkbp:bfsLayer |
7
|