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Found 4 papers, 2 papers with code
Weighted Sparse Subspace Representation: A Unified Framework for Subspace Clustering, Constrained Clustering, and Active Learning
Spectral-based subspace clustering methods have proved successful in many challenging applications such as gene sequencing, image recognition, and motion segmentation.
Subspace Clustering with Active Learning
In this paper, we propose an active learning framework for subspace clustering that sequentially queries informative points and updates the subspace model.
Minimum Spectral Connectivity Projection Pursuit
We study the problem of determining the optimal low dimensional projection for maximising the separability of a binary partition of an unlabelled dataset, as measured by spectral graph theory.
Minimum Density Hyperplanes
Associating distinct groups of objects (clusters) with contiguous regions of high probability density (high-density clusters), is central to many statistical and machine learning approaches to the classification of unlabelled data.
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