<jupyter_start><jupyter_text>Quickly Visualizing Multiview DataOften, before beginning extensive analysis, it is a good idea to visualizeyour dataset to help you understand its distribution and design algorithms.As a simple example, say we had high-dimensional multi-view data that wewanted to quickly visualize before we begin our analysis. With the*quick_visualize* function, we can easily do this. As an example, we willvisualize the UCI Multiple Features dataset, which consists of 6views of varying dimensionality.<jupyter_code># License: MIT

from mvlearn.plotting import quick_visualize
from mvlearn.datasets import load_UCImultifeature<jupyter_output><empty_output><jupyter_text>Visualize the Multiview DataWhile a 2D plot will never fully capture the essence of a 6-view dataset,this simple visualization helps us build intuition about the dataset byshowing us that it can be projected to a nearly-separable subspace.<jupyter_code># Load 4-class data
Xs, y = load_UCImultifeature(select_labeled=[0, 1, 2, 3])

# Visualize the data in 2D, coloring the points by their class label (0-3)
quick_visualize(Xs, labels=y, title='Labeled Data in 2D', figsize=(5, 5))<jupyter_output><empty_output>
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