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Pseudo-labels
Labels automatically generated by clustering algorithms to approximate true labels in a self-supervised learning context. They enable the transformation of unlabeled data into artificially labeled data for supervised training.
Self-supervised hierarchical clustering
Clustering method that builds a hierarchy of nested clusters without explicit supervision, used to generate pseudo-labels at different levels of granularity. This approach enables multi-scale exploration of data structure.
Self-supervised K-means
Variant of the classic K-means algorithm applied in a self-supervised framework to create pseudo-labels from unlabeled data. The resulting cluster centers then serve as prototypes for supervised training.
Adaptive DBSCAN
Enhanced version of DBSCAN that automatically adjusts its parameters based on local data density in a self-supervised context. This method enables the discovery of clusters with varied shapes and heterogeneous densities.
Semi-supervised spectral clustering
Clustering technique that uses eigenvalues of a similarity matrix to identify data structures, with automatically generated partial constraints. It combines spectral information with pseudo-labels to improve cluster coherence.
Automatic weak labeling
Process of generating imprecise but useful labels from intrinsic data features without human intervention. These weak labels serve as learning signals for robust supervised models.
Self-supervised contrastive learning
Learning paradigm where the model learns to distinguish similar samples (positives) from dissimilar samples (negatives) without explicit labels. Naturally formed clusters provide pseudo-labels for training.
Density-based clustering
Family of algorithms that identify clusters as dense regions separated by low-density regions in the feature space. This approach is particularly effective for discovering clusters of arbitrary shapes.
Iterative clustering algorithm
Clustering method that progressively refines pseudo-labels through multiple iterations of assignment and centroid updates. Each iteration improves intra-cluster cohesion and inter-cluster separation.
Internal cluster validation
Set of metrics evaluating the quality of generated clusters without reference to external labels, used to optimize pseudo-labels. These measures include the silhouette coefficient, Davies-Bouldin index, and Calinski-Harabasz score.
High-dimensional clustering
Technical challenge of grouping data in very high-dimensional spaces where the notion of distance loses its meaning. Specialized techniques such as dimensionality reduction are necessary for effective clustering.
Dimensionality reduction for clustering
Essential preliminary step in self-supervised clustering that transforms data into a lower-dimensional space while preserving cluster structure. It improves computational efficiency and the quality of pseudo-labels.
Graph-based clustering
Clustering approach that models data as a graph where nodes represent samples and edges represent similarities. Communities detected in this graph correspond to clusters used to generate pseudo-labels.
Affinity propagation clustering
Algorithm that identifies representative exemplars in the data and assigns each point to the most appropriate exemplar without requiring a predefined number of clusters. This method is particularly suited for discovering complex data structures.
Gaussian mixture clustering
Probabilistic approach that models data as a mixture of several Gaussian distributions, each component representing a cluster. Membership probabilities serve as soft pseudo-labels for supervised learning.
Incremental clustering
Clustering method capable of updating pseudo-labels as new data arrives without requiring complete recalculation. This approach is essential for continuous learning systems.
Multi-view clustering
Paradigm that integrates information from multiple representations or perspectives of the same data to improve cluster quality and pseudo-labels. This approach exploits the complementarity between different views for more robust learning.
Deep clustering
Combination of deep neural networks with clustering algorithms to learn optimal representations and generate pseudo-labels in an end-to-end manner. This approach enables the capture of complex non-linear structures in the data.