Papers Constrained Clustering
“Constrained Clustering” 태그가 달린 논문 72편 · 필터 해제
Partial Label Clustering
Partial label learning (PLL) is a significant weakly supervised learning framework, where each training example corresponds to a set of candidate labels and only one label is the ground-truth label. For the first time, t…
ClusteringConstrained ClusteringGraph LearningPartial Label Learning+1Enhancing Martian Terrain Recognition with Deep Constrained Clustering
Martian terrain recognition is pivotal for advancing our understanding of topography, geomorphology, paleoclimate, and habitability. While deep clustering methods have shown promise in learning semantically homogeneous f…
ClusteringConstrained ClusteringDeep ClusteringMetric LearningCycleGuardian: A Framework for Automatic RespiratorySound classification Based on Improved Deep clustering and Contrastive Learning
Auscultation plays a pivotal role in early respiratory and pulmonary disease diagnosis. Despite the emergence of deep learning-based methods for automatic respiratory sound classification post-Covid-19, limited datasets …
Audio ClassificationClusteringConstrained ClusteringContrastive Learning+2Clustering-Based Validation Splits for Model Selection under Domain Shift
This paper considers the problem of model selection under domain shift. Motivated by principles from distributionally robust optimisation (DRO) and domain adaptation theory, it is proposed that the training-validation sp…
ClusteringConstrained ClusteringDomain AdaptationModel Selection+1MC-GTA: Metric-Constrained Model-Based Clustering using Goodness-of-fit Tests with Autocorrelations
A wide range of (multivariate) temporal (1D) and spatial (2D) data analysis tasks, such as grouping vehicle sensor trajectories, can be formulated as clustering with given metric constraints. Existing metric-constrained …
ClusteringConstrained ClusteringFastLloyd: Federated, Accurate, Secure, and Tunable $k$-Means Clustering with Differential Privacy
We study the problem of privacy-preserving $k$-means clustering in the horizontally federated setting. Existing federated approaches using secure computation suffer from substantial overheads and do not offer output priv…
ClusteringConstrained ClusteringPrivacy PreservingTowards Explainable Clustering: A Constrained Declarative based Approach
The domain of explainable AI is of interest in all Machine Learning fields, and it is all the more important in clustering, an unsupervised task whose result must be validated by a domain expert. We aim at finding a clus…
ClusteringConstrained ClusteringExtreme Point Pursuit -- Part II: Further Error Bound Analysis and Applications
In the first part of this study, a convex-constrained penalized formulation was studied for a class of constant modulus (CM) problems. In particular, the error bound techniques were shown to play a vital role in providin…
Constrained ClusteringGraph MatchingA consensus-constrained parsimonious Gaussian mixture model for clustering hyperspectral images
The use of hyperspectral imaging to investigate food samples has grown due to the improved performance and lower cost of instrumentation. Food engineers use hyperspectral images to classify the type and quality of a food…
ClusteringComputational EfficiencyConstrained ClusteringLarge Scale Constrained Clustering With Reinforcement Learning
Given a network, allocating resources at clusters level, rather than at each node, enhances efficiency in resource allocation and usage. In this paper, we study the problem of finding fully connected disjoint clusters to…
ClusteringCombinatorial OptimizationConstrained Clusteringreinforcement-learning+1Advanced Image Segmentation Techniques for Neural Activity Detection via C-fos Immediate Early Gene Expression
This paper investigates the application of advanced image segmentation techniques to analyze C-fos immediate early gene expression, a crucial marker for neural activity. Due to the complexity and high variability of neur…
Action DetectionActivity DetectionClusteringConstrained Clustering+3ConstraintMatch for Semi-constrained Clustering
Constrained clustering allows the training of classification models using pairwise constraints only, which are weak and relatively easy to mine, while still yielding full-supervision-level model performance. While they p…
ClusteringConstrained ClusteringMulti‑camera trajectory matching based on hierarchical clustering and constraints
The fast improvement of deep learning methods resulted in breakthroughs in image classification, object detection, and object tracking. Autonomous driving and traffic monitoring systems, especially the on-premise install…
AttributeAutonomous DrivingConstrained Clusteringimage-classification+8Incremental Constrained Clustering by Minimal Weighted Modification
Clustering is a well-known task in Data Mining that aims at grouping data instances according to their similarity. It is an exploratory and unsupervised task whose results depend on many parameters, often requiring the e…
ClusteringConstrained ClusteringIncremental Constrained ClusteringTime SeriesDeep Clustering with Incomplete Noisy Pairwise Annotations: A Geometric Regularization Approach
The recent integration of deep learning and pairwise similarity annotation-based constrained clustering -- i.e., $\textit{deep constrained clustering}$ (DCC) -- has proven effective for incorporating weak supervision int…
ClusteringConstrained ClusteringDeep ClusteringUniversal Weak Coreset
Coresets for $k$-means and $k$-median problems yield a small summary of the data, which preserve the clustering cost with respect to any set of $k$ centers. Recently coresets have also been constructed for constrained $k…
ClusteringConstrained ClusteringData CompressionSemi-Supervised Constrained Clustering: An In-Depth Overview, Ranked Taxonomy and Future Research Directions
Clustering is a well-known unsupervised machine learning approach capable of automatically grouping discrete sets of instances with similar characteristics. Constrained clustering is a semi-supervised extension to this p…
ClusteringConstrained ClusteringNeural Capacitated Clustering
Recent work on deep clustering has found new promising methods also for constrained clustering problems. Their typically pairwise constraints often can be used to guide the partitioning of the data. Many problems however…
ClusteringConstrained ClusteringDeep ClusteringOptimal Decision Trees For Interpretable Clustering with Constraints (Extended Version)
Constrained clustering is a semi-supervised task that employs a limited amount of labelled data, formulated as constraints, to incorporate domain-specific knowledge and to significantly improve clustering accuracy. Previ…
ClusteringConstrained ClusteringSpatiotemporal Classification with limited labels using Constrained Clustering for large datasets
Creating separable representations via representation learning and clustering is critical in analyzing large unstructured datasets with only a few labels. Separable representations can lead to supervised models with bett…
ClusteringConstrained ClusteringRepresentation Learning