Constrained Clustering
25 papers with code • 0 benchmarks • 0 datasets
Split data into groups, taking into account knowledge in the form of constraints on points, groups of points, or clusters.
Benchmarks
These leaderboards are used to track progress in Constrained Clustering
Most implemented papers
A Framework for Deep Constrained Clustering
blueocean92/deep_constrained_clustering
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A fundamental strength of deep learning is its flexibility, and here we explore a deep learning framework for constrained clustering and in particular explore how it can extend the field of constrained clustering.
Advances in integration of end-to-end neural and clustering-based diarization for real conversational speech
nttcslab-sp/eend-vector-clustering
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This paper is to (1) report recent advances we made to this framework, including newly introduced robust constrained clustering algorithms, and (2) experimentally show that the method can now significantly outperform competitive diarization methods such as Encoder-Decoder Attractor (EDA)-EEND, on CALLHOME data which comprises real conversational speech data including overlapped speech and an arbitrary number of speakers.
Spatially relaxed inference on high-dimensional linear models
This calls for a reformulation of the statistical inference problem, that takes into account the underlying spatial structure: if covariates are locally correlated, it is acceptable to detect them up to a given spatial uncertainty.
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.
Deep Conditional Gaussian Mixture Model for Constrained Clustering
lauramanduchi/DC-GMM
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NeurIPS 2021
Constrained clustering has gained significant attention in the field of machine learning as it can leverage prior information on a growing amount of only partially labeled data.
FP-Age: Leveraging Face Parsing Attention for Facial Age Estimation in the Wild
ibug-group/fpage
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To evaluate our method on in-the-wild data, we also introduce a new challenging large-scale benchmark called IMDB-Clean.
An Exact Algorithm for Semi-supervised Minimum Sum-of-Squares Clustering
The minimum sum-of-squares clustering (MSSC), or k-means type clustering, is traditionally considered an unsupervised learning task.
Constrained Clustering and Multiple Kernel Learning without Pairwise Constraint Relaxation
However, the common practice of relaxing discrete constraints to a continuous domain to ease optimization when learning kernels or metrics can harm generalization, as information which only encodes linkage is transformed to informing distances.
A Bibliographic View on Constrained Clustering
A keyword search on constrained clustering on Web-of-Science returned just under 3, 000 documents.
Neural Capacitated Clustering
jokofa/ncc
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Recent work on deep clustering has found new promising methods also for constrained clustering problems.
