Multiview Coregularized Spectral Clustering Comparison

Here we will compare the performance of the Co-Regularized multiview and singleview spectral clustering. We will evaluate the purity of the resulting clusters from each algorithm with respect to the class labels using the normalized mutual information metric.

As we can see, Co-Regularized multiview clustering produces clusters with higher purity compared to those produced by singleview clustering for all 3 input types.

# License: MIT

import warnings
import numpy as np
from sklearn.cluster import SpectralClustering
from sklearn.metrics import normalized_mutual_info_score as nmi_score
from mvlearn.datasets import load_UCImultifeature
from mvlearn.cluster import MultiviewCoRegSpectralClustering
from mvlearn.plotting import quick_visualize

warnings.simplefilter('ignore')  # Ignore warnings
RANDOM_SEED = 10


# Load the UCI Digits Multiple Features Data Set as an Example Data Set
# Load dataset along with labels for digits 0 through 4
n_class = 5
Xs, labels = load_UCImultifeature(
    select_labeled=list(range(n_class)), views=[0, 1])

Singleview spectral clustering

Cluster each view separately and compute nmi

s_spectral = SpectralClustering(
    n_clusters=n_class, random_state=RANDOM_SEED, n_init=100)

for i in range(len(Xs)):
    s_clusters = s_spectral.fit_predict(Xs[i])
    s_nmi = nmi_score(labels, s_clusters, average_method='arithmetic')
    print('Single-view View {0:d} NMI Score: {1:.3f}\n'.format(i + 1, s_nmi))

# Concatenate the multiple views into a single view and produce clusters
s_data = np.hstack(Xs)
s_clusters = s_spectral.fit_predict(s_data)

s_nmi = nmi_score(labels, s_clusters)
print('Single-view Concatenated NMI Score: {0:.3f}\n'.format(s_nmi))

Out:

Single-view View 1 NMI Score: 0.620

Single-view View 2 NMI Score: 0.007

Single-view Concatenated NMI Score: 0.007

Co-Regularized multiview spectral clustering

Use the MultiviewSpectralClustering instance to cluster the data

m_spectral1 = MultiviewCoRegSpectralClustering(n_clusters=n_class,
                                               random_state=RANDOM_SEED,
                                               n_init=100)
m_clusters1 = m_spectral1.fit_predict(Xs)

# Compute nmi between true class labels and multi-view cluster labels
m_nmi1 = nmi_score(labels, m_clusters1)
print('Multi-view NMI Score: {0:.3f}\n'.format(m_nmi1))

Out:

Multi-view NMI Score: 0.663

Visualize Results

Plots of clusters produced by multiview spectral clustering and the true clusters.

We will display the clustering results of the Co-Regularized multiview spectral clustering algorithm below, along with the true class labels.

quick_visualize(Xs, labels=labels, title='Ground Truth',
                scatter_kwargs={'s': 8})
quick_visualize(Xs, labels=m_clusters1, title='Multi-view Clustering',
                scatter_kwargs={'s': 8})

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