Optimally registers and clusters joint images using multivariate information.
problem Joint image registration and clustering in limited-resolution images.
method Asymptotically optimal algorithms based on multivariate information functionals.
result Order-optimal registration and clustering of numerous images.
Joint learning framework for clustering and graph construction.
problem Graph construction not fitting clustering requirements.
method Joint learning framework to learn graph and clustering simultaneously.
result Improved clustering accuracy on 10 datasets.
JECL clusters images and captions by jointly learning representations and assignments.
problem Clustering image-caption pairs with limited structured training data.
method Parallel encoders trained with clustering and alignment objectives, minimizing KL divergence and maximizing Jensen-Shannon divergence, with regularizers.
result JECL outperforms single-view and multi-view methods on large image-caption datasets.
New method jointly clusters and learns representations for better performance.
problem Jointly clustering and learning representations for improved clustering performance.
method Continuous reparametrization of k-Means objective function. result Jointly clustering and learning representations leads to better performance.
AI-enabled precision medicine promises a transformational improvement in healthcare outcomes by enabling data-driven personalized diagnosis, prognosis, and treatment. However, the well-known "curse of dimensionality" and the clustered structure of biomedical data together interact to present a joint challenge in the hi…
This paper introduces a new method to cluster qualitative attribute data using tree structures.
problem Clustering qualitative attribute data, especially when values are not in Euclidean space.
method Developed a joint learning mechanism to iteratively learn trees representing qualitative values' order relationships.
result The joint learning mechanism successfully clusters qualitative attribute data, yielding accurate results.
NeuralFLoC unifies registration and clustering of functional data, overcoming phase variation challenges.
problem Challenges in clustering functional data due to phase variation and temporal misalignment.
method NeuralFLoC uses Neural ODE-driven diffeomorphic flows and spectral clustering for joint registration and clustering.
result NeuralFLoC effectively disentangles phase and amplitude variation, achieving state-of-the-art performance.
Paper proposes a new method for joint feature selection and graph learning.
problem Previous methods suffer from neglecting joint formulation and lack of graph learning.
method Formulates multi-view feature selection with orthogonal decomposition, incorporates cross-space locality preservation, and uses a unified objective function for simultaneous learning.
result Demonstrates superior performance in multi-view feature selection and graph learning tasks.
New computational methods improve clustering of objects.
problem Clustering objects based on their joint occurrence in sets.
method Genetic algorithm with renumbering, local search, and simplified model.
result Improvements enhance computational performance of genetic algorithm.
Develops a model to detect shared communities in non-aligned graphs.
problem Clustering and community detection in non-aligned graphs with heterogeneous populations.
method Joint Stochastic Blockmodel (Joint SBM) and efficient spectral clustering.
result The joint model better estimates communities compared to separate SBMs on individual graphs.
A new method for multilevel clustering using Wasserstein distances.
problem Simultaneously partitioning data in groups and discovering group patterns.
method Joint optimization over spaces of discrete probability measures with Wasserstein distances.
result Consistency properties for estimates of local and global clusters established.
A new method for multilevel clustering using Wasserstein means.
problem Simultaneously partitioning data in each group and discovering grouping patterns among groups.
method Joint optimization over spaces of discrete probability measures with Wasserstein distance metrics, including variants that admit fast optimization.
result Consistency properties for estimates of both local and global clusters are established.
SOEM clusters time series data with improved accuracy.
problem Clustering non-aligned time series data.
method Generalizes SOFM to matrix input using approximate joint diagonalisation of covariance structures.
result SOEM produces valid topological clustering of time series data.
The paper proposes a parallelizable clustering method for multivariate data.
problem The standard model-based clustering method assumes the same number of clusters per margin, which is often unrealistic.
method Developed a finite mixture model per margin with different numbers of clusters, and used a game-inspired algorithm to cluster multivariate data.
result The proposed method shows good performance in various scenarios and real datasets.
Study quantifies motion dynamics of ankle sprains using biosensor data.
problem Diagnosing chronic ankle instability (CAI) based on objective biomechanical measures.
method Developed a nonlinear subspace clustering method to learn motion patterns from multi-joint coordination.
result Classification accuracy of >70% on CAI vs. normal controls using leave-one-subject-out cross validation.
FL+HC improves federated learning on non-iid data by clustering local updates.
problem FL struggles with non-iid data, leading to suboptimal models.
method Introduce hierarchical clustering to separate and train clusters of clients independently.
result FL+HC converges faster and achieves higher accuracy than standard FL.
Solves complex clustering and rotation synchronization problem.
problem Challenges in classifying and synchronizing rotated objects into multiple categories.
method Semidefinite programming relaxations to solve the joint problem of community detection and synchronization.
result Exact recovery of community detection and synchronization when extending stochastic block model.
Improves text clustering by incorporating sequential features and word embeddings.
problem Lack of sequential information and synonym handling in current text clustering methods.
method SiDPMM model that models documents as joint of bags of words, sequential features, and word embeddings.
result Significant improvement in performance and accurate inference of cluster numbers.
A new framework learns clustering and dimensionality reduction together.
problem Challenges in clustering high-dimensional data.
method Gradient-based manifold optimization for joint learning.
result Better performance compared to existing clustering algorithms.
Study characterizes cryospheric spectral feature space using joint PC+t-SNE approach.
problem Characterize cryospheric spectral feature space for remote sensing applications.
method Compare and contrast two approaches for identifying feature space basis vectors via dimensionality reduction (PCA and t-SNE).
result Joint characterization reveals distinct continua and clusters of ice reflectance properties.
Dimensionality reduction techniques play an essential role in data analytics, signal processing and machine learning. Dimensionality reduction is usually performed in a preprocessing stage that is separate from subsequent data analysis, such as clustering or classification. Finding reduced-dimension representations tha…
Develops a method for extracting sources in multi-subject fMRI data.
problem Analyzing brain imaging datasets from multiple subjects with varying levels of jointness.
method Deflation-based algorithm using higher order cumulants and thin-SVD factorization.
result The algorithm accurately identifies joint, partially-joint, and individual sources with high precision.
Transform learning improves K-means clustering for document analysis.
problem Improving K-means clustering for document analysis.
method Embedding K-means clustering loss into transform learning framework and solving jointly using ADMM.
result Improves over state-of-the-art in document clustering.
Paper links network Lasso to network flow optimization.
problem Joint clustering and optimization of networked data.
method Exploration of duality between network Lasso and network flow optimization.
result nLasso is equivalent to a minimum-cost flow problem on the data network structure.
New algorithm clusters sparse data effectively.
problem Challenges in clustering sparse data.
method Deterministic Information Bottleneck framework for joint feature weighting and clustering.
result Demonstrated effectiveness on real-world genomics data.
Unified kernel approach for similarity and clustering.
problem Challenges in similarity measurement and nonlinear similarity.
method Simultaneously learns cluster indicator matrix and similarity information in kernel spaces.
result Automatic optimization of cluster indicator matrix, similarity relations, and kernel combination.
MIM learns joint distributions with mutual information and low divergence.
problem Learning joint distributions over observations and latent variables.
method Probabilistic auto-encoder with three design principles: low divergence, high mutual information, and low marginal entropy.
result MIM learns representations with high mutual information, consistent encoding and decoding distributions, effective latent clustering, and comparable data log likelihood to VAE.
Method learns cluster structure while estimating heterogeneous graphical models.
problem Joint estimation of multiple graphical models from heterogeneous and high-dimensional data.
method High-dimensional ECM algorithm with joint graphical lasso penalty.
result Demonstrated superior performance and new insights in Glioblastoma cancer dataset.
Hybrid method for clustering text and graph data.
problem Discover latent connections and content associations in mixed data.
method Joint Nonnegative Matrix Factorization (NMF) for text and graph clustering.
result Higher quality clustering results compared to methods using only content or structure.
Enhances curve alignment for diverse data types.
problem Aligning curve data effectively.
method Developed nonlinear transformations for curve data.
result Successfully aligned synthetic and real curve data.
Improved clustering with outlier detection using cardinality constraints.
problem Outlier sensitivity and unbalanced clusters in K-means.
method Formulates a joint outlier detection and clustering problem as a mixed-integer linear program (MILP) with cardinality constraints.
result Proves the optimality of solutions under certain conditions.
The task of clustering a set of objects based on multiple sources of data arises in several modern applications. We propose an integrative statistical model that permits a separate clustering of the objects for each data source. These separate clusterings adhere loosely to an overall consensus clustering, and hence the…
The paper introduces the concept of a cluster structure to define a joint distribution of the sample size and its exchangeable random partitions. The cluster structure allows the probability distribution of the random partitions of a subset of the sample to be dependent on the sample size, a feature not presented in a …
CCC clusters with controlled spread, outperforming standard methods.
problem Compact and structured clustering with spread control.
method Constrained Centroid Clustering using Lagrangian formulation.
result CCC achieves more compact clusters with reduced radial spread.
Observational data usually comes with a multimodal nature, which means that it can be naturally represented by a multi-layer graph whose layers share the same set of vertices (users) with different edges (pairwise relationships). In this paper, we address the problem of combining different layers of the multi-layer gra…
Paper tackles joint community detection and phase synchronization in stochastic block models.
problem Jointly recover cluster structure and phase angles in stochastic block models.
method Proposes two algorithms: a spectral method based on multi-frequency QR factorization and an iterative multi-frequency generalized power method.
result Proposed algorithms significantly improve recovery of cluster structure and phase angles compared to existing methods.
A new clustering method using deep autoencoder networks and spectral clustering.
problem Improving clustering accuracy in noisy data.
method Dual autoencoder network for robust latent representations, mutual information estimation for discriminative features, deep spectral clustering.
result Significantly outperforms state-of-the-art clustering approaches on benchmark datasets.
FCA improves fair clustering by optimizing utility and fairness.
problem Balancing fairness and utility in clustering.
method FCA alternates between aligning data and optimizing cluster centers in an aligned space.
result FCA achieves a superior trade-off between fairness and utility.
Joint alignment of a collection of functions is the process of independently transforming the functions so that they appear more similar to each other. Typically, such unsupervised alignment algorithms fail when presented with complex data sets arising from multiple modalities or make restrictive assumptions about the …
HCRL learns hierarchical embeddings from deep embeddings of hierarchy components.
problem Flat clustering limits cohesive instance relations in hierarchical data.
method Simultaneously optimizes representation learning and hierarchical clustering in the embedding space.
result HCRL achieves best hierarchical clustering and data reconstruction.
Unsupervised clustering of curves according to their shapes is an important problem with broad scientific applications. The existing model-based clustering techniques either rely on simple probability models (e.g., Gaussian) that are not generally valid for shape analysis or assume the number of clusters. We develop an…
AdapVAE learns streaming data clustering and feature learning adaptively.
problem Adaptive clustering and feature learning for streaming data.
method Bayesian Nonparametric (BNP) modeling with Deep Neural Networks (DNNs) for feature learning, online variational inference algorithm.
result AdapVAE can adaptively detect novel clusters in emerging data without catastrophic forgetting.
Proposes a method to cluster tasks for constructive cooperative multi-tasking.
problem Destructive cooperation in cooperative multi-tasking.
method Semantic clustering followed by end-to-end joint training within clusters.
result Effective mitigation of destructive cooperation and negative transfer.
Sparse GEMINI selects relevant features for clustering without assumptions.
problem Feature selection in clustering with relevant clusters and variables.
method Discriminative clustering model maximizing GEMINI with l1 penalty.
result Sparse GEMINI selects relevant subsets of variables without prior hypotheses.
New distributed clustering algorithms show resilience to initialization issues.
problem Resilience of distributed gradient-based clustering algorithms to center initialization.
method Distributed gradient-based clustering algorithms with novel center initialization.
result The algorithms are more resilient to initialization compared to baseline methods.
Paper introduces a method for supervised hierarchical clustering with Exponential Linkage.
problem Discrepancy between training and clustering objectives in supervised clustering.
method Tightly couples supervised training of dissimilarity function with hierarchical clustering, using Exponential Linkage.
result Joint training procedure consistently matches or outperforms other methods, improving dendrogram purity by up to 8 points.
We propose a kernel method to identify finite mixtures of nonparametric product distributions. It is based on a Hilbert space embedding of the joint distribution. The rank of the constructed tensor is equal to the number of mixture components. We present an algorithm to recover the components by partitioning the data p…
A deep learning approach for clustering time series of varying lengths.
problem Clustering time series with variable lengths and temporal relations.
method Recurrent Deep Divergence-based Clustering framework.
result Outperforms previous methods on benchmark datasets.