Multi-View Co-Training Spectral Clustering

class polyview.cluster.mv_cotrain_sc.MultiViewCoTrainSpectralClustering(*args: Any, **kwargs: Any)

Bases: BaseMultiViewClusterer

Multi-view co-training spectral clustering algorithm.

Parameters:

  • n_clusters (int, default=2) – The number of clusters to form.

  • n_init (int, default=10) – Number of time the k-means algorithm will be run with different centroid seeds.

  • max_iter (int, default=50) – Maximum number of iterations of the alternating optimization.

  • affinity (str, default='rbf') – Kernel to use for computing the affinity matrix. Should be a valid metric for sklearn.metrics.pairwise.pairwise_kernels.

  • lambda_reg (float, default=1.0) – Regularization parameter for co-training terms.

  • random_state (int or None, default=None) – Determines random number generation for centroid initialization. Use an int to make the randomness deterministic.

embedding_

The concatenated spectral embeddings from all views after fitting.

Type:

np.ndarray of shape (n_samples, n_clusters * n_views)

objective_

The objective function values at each iteration of the optimization process.

Type:

list of float

labels_

Cluster labels for each sample after fitting.

Type:

np.ndarray of shape (n_samples,)

References

Kumar A. and Daumé H. (2011). A Co-training Approach for Multi-view Spectral Clustering. In Proceedings of the 28th International Conference on Machine Learning (ICML-11).

fit(views: List[numpy.ndarray]) → None
fit_predict(views: List[numpy.ndarray], y=None) → numpy.ndarray

Fit and return cluster labels.

Parameters:

  • views (list of array-like)

  • y (ignored)

Returns:

labels

Return type:

ndarray of shape (n_samples,)