SKPCA python

SKPCA (Supervised Kernel Principal Component Analysis) is a variant of Kernel Principal Component Analysis that incorporates class information to guide the dimensionality reduction process. In SKPCA, the class labels of the data points are utilized to maximize the separation between different classes in the lower-dimensional feature space.

To implement SKPCA in Python, you can use the scikit-learn library. Here's an example:

from sklearn.decomposition import KernelPCA
from sklearn.datasets import make_classification

# Generate some random classification data
X, y = make_classification(n_samples=100, n_features=10, n_classes=2)

# Perform SKPCA
skpca = KernelPCA(n_components=2, kernel='rbf')
X_skpca = skpca.fit_transform(X, y)

# Plot the transformed data
import matplotlib.pyplot as plt
plt.scatter(X_skpca[:, 0], X_skpca[:, 1], c=y)
plt.xlabel("First Component")
plt.ylabel("Second Component")
plt.title("SKPCA")
plt.show()

In this example, we first generate some random classification data using `make_classification` from scikit-learn. Then, we create an instance of `KernelPCA` and specify the desired number of components and kernel type. Finally, we fit the model with the data and labels using `fit_transform` and plot the transformed data.

Note that you may need to install scikit-learn and matplotlib if you haven't already done so. You can use pip to install them:

pip install scikit-learn matplotlib

I hope this helps! Let me know if you have any further questions.