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
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Most implemented papers
Learning Discrete Representations via Constrained Clustering for Effective and Efficient Dense Retrieval
However, the efficiency of most existing DR models is limited by the large memory cost of storing dense vectors and the time-consuming nearest neighbor search (NNS) in vector space.
Fast Computation of Wasserstein Barycenters
We present new algorithms to compute the mean of a set of empirical probability measures under the optimal transport metric.
Towards Uncovering the Intrinsic Data Structures for Unsupervised Domain Adaptation using Structurally Regularized Deep Clustering
To address this issue, we are motivated by a UDA assumption of structural similarity across domains, and propose to directly uncover the intrinsic target discrimination via constrained clustering, where we constrain the clustering solutions using structural source regularization that hinges on the very same assumption.
On Constrained Spectral Clustering and Its Applications
Furthermore, by inheriting the objective function from spectral clustering and encoding the constraints explicitly, much of the existing analysis of unconstrained spectral clustering techniques remains valid for our formulation.
Learning to cluster in order to transfer across domains and tasks
GT-RIPL/L2C
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ICLR 2018
The key insight is that, in addition to features, we can transfer similarity information and this is sufficient to learn a similarity function and clustering network to perform both domain adaptation and cross-task transfer learning.
A Framework for Deep Constrained Clustering -- Algorithms and Advances
blueocean92/deep_constrained_clustering
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The area of constrained clustering has been extensively explored by researchers and used by practitioners.
SPONGE: A generalized eigenproblem for clustering signed networks
We introduce a principled and theoretically sound spectral method for $k$-way clustering in signed graphs, where the affinity measure between nodes takes either positive or negative values.
Deep Constrained Dominant Sets for Person Re-identification
leule/DCDS
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ICCV 2019
By optimizing the constrained clustering in an end-to-end manner, we naturally leverage the contextual knowledge of a set of images corresponding to the given person-images.
Constrained Clustering: General Pairwise and Cardinality Constraints
Extensive experiments on both synthetic and real data demonstrate when: (1) utilizing a single category of constraint, the proposed model is superior to or competitive with SOTA constrained clustering models, and (2) utilizing both categories of constraints jointly, the proposed model shows better performance than the case of the single category.
Statistical control for spatio-temporal MEG/EEG source imaging with desparsified multi-task Lasso
To deal with this, we adapt the desparsified Lasso estimator -- an estimator tailored for high dimensional linear model that asymptotically follows a Gaussian distribution under sparsity and moderate feature correlation assumptions -- to temporal data corrupted with autocorrelated noise.
