t-SNE#

t-SNE (t-distributed stochastic neighbor embedding) is a popular dimensionality reduction technique for visualizing high-dimensional data. Unlike other dimensionality reduction techniques, t-SNE does not focus on preserving the linear relationships between the data points but instead focuses on preserving the local structure of the data points. This makes t-SNE an ideal choice for visualizing complex and non-linear data.

How Does t-SNE Work?#

t-SNE works by reducing the dimensionality of the data while maintaining the relationships between the data points. It maps the data points in a high-dimensional space to a low-dimensional space in such a way that similar data points are close to each other in the low-dimensional space. t-SNE uses a stochastic optimization procedure to find the best mapping between the high-dimensional data and the low-dimensional space.

The optimization procedure used by t-SNE involves two main steps: first, it computes the probability of similarity between each pair of data points in the high-dimensional space, and second, it computes the probability of similarity between each pair of data points in the low-dimensional space. The t-SNE algorithm then minimizes the difference between the two similarity probabilities to find the best mapping between the high-dimensional data and the low-dimensional space.

Advantages#

1. t-SNE is effective in preserving the local structure of the data points, making it ideal for visualizing complex and non-linear data.

2. t-SNE is easy to use and implement, with a wide range of implementations available in various programming languages.

3. t-SNE is highly customizable, allowing users to adjust various parameters to get the best results for their data.

Disadvantages#

1. t-SNE is computationally expensive, making it unsuitable for very large datasets.

2. t-SNE is sensitive to the initial conditions, meaning that different initial conditions can result in different final mappings.

3. t-SNE is not guaranteed to preserve the global structure of the data, making it less suitable for some applications.

Example Code#

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

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

# Apply t-SNE to the iris data
tsne = TSNE(n_components=2, random_state=0)
X_tsne = tsne.fit_transform(X)

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


Conclusion#

t-SNE is a powerful and widely used technique for visualizing high-dimensional data. Its ability to preserve the local structure of the data makes it ideal for exploring complex and non-linear relationships in the data. While t-SNE has some limitations, such as sensitivity to initial conditions and computational cost, its wide range of applications and ease of use make it a valuable tool for data scientists and researchers.

Where to Learn More#

I’ve covered t-SNE in-depth in the following course:

Unsupervised Deep Learning in Python