methodology-6

Constrained Clustering

3260 papers • 126 benchmarks • 313 datasets

Split data into groups, taking into account knowledge in the form of constraints on points, groups of points, or clusters.

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Only Connect Walls Dataset Task 1 (Grouping)Incremental Constrained Clustering

Most implemented papers

Learning Discrete Representations via Constrained Clustering for Effective and Efficient Dense Retrieval

M. Zhang, Shaoping Ma, Jingtao Zhan, Jiafeng Guo, Yiqun Liu, Jiaxin Mao•Mon Oct 11 2021

RepCONC is a novel retrieval model that learns discrete Representations via CONstrained Clustering and substantially outperforms a wide range of existing retrieval models in terms of retrieval effectiveness, memory efficiency, and time efficiency.

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Fast Computation of Wasserstein Barycenters

A. Doucet, Marco Cuturi•Tue Oct 15 2013

The Wasserstein distance is proposed to be smoothed with an entropic regularizer and recover in doing so a strictly convex objective whose gradients can be computed for a considerably cheaper computational cost using matrix scaling algorithms.

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Towards Uncovering the Intrinsic Data Structures for Unsupervised Domain Adaptation Using Structurally Regularized Deep Clustering

Xiatian Zhu, K. Jia, Hui Tang, C. L. P. Chen•Mon Dec 07 2020

This work proposes a hybrid model of Structurally Regularized Deep Clustering, which integrates the regularized discriminative clustering of target data with a generative one, and term the method as H-SRDC, which outperforms all the existing methods under both the inductive and transductive settings.

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On constrained spectral clustering and its applications

I. Davidson, Xiang Wang, B. Qian•Tue Jan 24 2012

This paper presents a more natural and principled formulation of constrained spectral clustering, which explicitly encodes the constraints as part of a constrained optimization problem, and demonstrates an innovative use of encoding large number of constraints: transfer learning via constraints.

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Learning to cluster in order to Transfer across domains and tasks

Z. Kira, Zhaoyang Lv, Yen-Chang Hsu•Mon Nov 27 2017

A novel method to perform transfer learning across domains and tasks, formulating it as a problem of learning to cluster with state of the art results for the challenging cross-task problem, applied on Omniglot and ImageNet.

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SPONGE: A generalized eigenproblem for clustering signed networks

Mihai Cucuringu, Peter Davies, Aldo Glielmo, Hemant Tyagi•Mon Apr 15 2019

This work introduces 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.

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Deep Constrained Dominant Sets for Person Re-Identification

M. Shah, M. Pelillo, L. T. Alemu•Wed Apr 24 2019

This work proposes an intriguing scheme which treats person-image retrieval problem as a constrained clustering optimization problem, called deep constrained dominant sets (DCDS), and shows that the proposed method can outperform state-of-the-art methods.

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Constrained Clustering: General Pairwise and Cardinality Constraints

Bernard Ghanem, Adel Bibi, A. Alqahtani•Tue Jul 23 2019

The experimental results show that the proposed method exploits the constraints to achieve perfect clustering performance with improved clustering to $2-5$ % in classical clustering metrics, e.g., Adjusted Random Index (ARI), Mirkin's Index (MI), and Huber’s Index (HI), outerperfomring all compared-againts methods across the board.

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Statistical control for spatio-temporal MEG/EEG source imaging with desparsified multi-task Lasso

Alexandre Gramfort, B. Thirion, Jérôme-Alexis Chevalier, J. Salmon•Mon Sep 28 2020

Extensive simulations on realistic head geometries, as well as empirical results on various MEG datasets, demonstrate the high recovery performance of ecd-MTLasso and its primary practical benefit: offer a statistically principled way to threshold MEG/EEG source maps.

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