t-Distributed Stochastic Neighbor Embedding (t-SNE)

t-SNE (t-Distributed Stochastic Neighbor Embedding) is a popular dimensionality reduction technique for visualizing high-dimensional data. It works by transforming the data into a low-dimensional representation in such a way that similar instances are modeled as close neighbors in the low-dimensional space.

Here’s an example of using t-SNE in Python using the scikit-learn library:

import matplotlib.pyplot as plt
from sklearn import datasets
from sklearn.manifold import TSNE

# Load the iris dataset
iris = datasets.load_iris()
X = iris.data
y = iris.target

# Perform t-SNE
tsne = TSNE(n_components=2)
X_tsne = tsne.fit_transform(X)

# Plot the result
plt.scatter(X_tsne[:, 0], X_tsne[:, 1], c=y)
plt.show()

In this example, we first load the iris dataset from scikit-learn’s datasets module. Then we use the t-SNE class from the sklearn.manifold module to perform the t-SNE transformation on the data. Finally, we use matplotlib to plot the result and visualize the low-dimensional representation.

t-SNE (Distributed Stochastic Neighbor Embedding) by Mehmet Akif CIFCI

The t-SNE algorithm is highly sensitive to the choice of hyperparameters, such as the number of dimensions, perplexity, and learning rate. Experimenting with different hyperparameters can result in significantly different visualizations.