Machine Learning for OpenCV 4 - Second Edition

Visualizing the dimensionality reduction using t-Distributed Stochastic Neighbor Embedding (t-SNE)

t-SNE is a technique for dimensionality reduction that is best suited to the visualization of high-dimensional data.

In this section, we will see an example of how to visualize high-dimensional datasets using t-SNE. Let's use the digits dataset in this case, which has handwritten images of digits from 0 to 9. It's a publicly available dataset, commonly referred to as the MNIST dataset. We will see how we can visualize the dimensionality reduction on this dataset using t-SNE:

1. First, let's load the dataset:

In [1]: import numpy as npIn [2]: from sklearn.datasets import load_digitsIn [3]: digits = load_digits()In [4]: X, y = digits.data/255.0, ...