An algorithm for clustering data from group-invariant subspaces.
problem Clustering data from a union of group-invariant subspaces.
method Sparse Sub-module Clustering (SSmC) based on group-sparse self-representation.
result General conditions for identifying group-invariant subspaces.
Simpler approach for sparse clustering.
problem Sparse clustering with only useful features.
method Hill-climbing approach to Sparse K-means.
result Competitive with COSA and Sparse K-means.
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.
Sparse Convex Clustering improves clustering performance in high-dimensional data.
problem Distortion in convex clustering performance with uninformative features.
method Introduces Sparse Convex Clustering with an adaptive group-lasso penalty and a tuning criterion based on clustering stability.
result Demonstrates improved clustering performance through feature selection.
Sparse PCA method for clustering Gaussian mixtures.
problem Clustering Gaussian mixture models.
method Sparse Principal Component Analysis (SPCA) for clustering.
result Comparison with IF-PCA method and discussion of non-diagonal covariance matrices.
This paper improves OMP-based sparse subspace clustering with data-adaptive capability.
problem Existing OMP-based approaches lack data adaptiveness, leading to inaccurate data representation.
method Develops a parameter selection process to adjust OMP parameters based on data distribution and introduces a new SEA ratio metric.
result Proposed approach achieves better clustering accuracy, SEA ratio, and representation quality compared to other OMP-based methods.
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.
Unified method for simultaneous denoising and clustering.
problem Clustering noisy signals.
method Sparse convex wavelet clustering with fusion and group-sparse penalties.
result Unified approach that denoises and clusters simultaneously.
Paper addresses graph connectivity issues in noisy sparse subspace clustering.
problem Graph connectivity problem in noisy sparse subspace clustering.
method Proposes a simple post-processing procedure to ensure graph connectivity.
result Demonstrates consistency of clustering under certain assumptions.
Network Lasso clusters sparse graph clusters efficiently.
problem Local graph clustering of sparse and chain-like clusters.
method Network Lasso minimizes total variation of cluster indicator signals.
result Network Lasso handles sparse clusters difficult for spectral clustering.
Paper optimizes Laplacian regularization for sparse network clustering.
problem Improving spectral clustering in sparse networks.
method Formally determines optimal Laplacian regularization.
result Proper regularization is closely tied to state-of-the-art techniques.
Bayesian method clusters data and selects variables with shrinkage priors.
problem Sparse convex clustering with limited data accuracy issues.
method Bayesian approach using global-local shrinkage priors and Gibbs sampling.
result Improved estimation accuracy in sparse convex clustering.
Stochastic Sparse Subspace Clustering improves subspace clustering by reducing over-segmentation through dropout.
problem Over-segmentation in subspace clustering.
method Introducing dropout regularization to enforce denser connections between points from the same subspace.
result Stochastic Sparse Subspace Clustering effectively handles large datasets and reduces over-segmentation.
There has been a surge in the number of large and flat data sets - data sets containing a large number of features and a relatively small number of observations - due to the growing ability to collect and store information in medical research and other fields. Hierarchical clustering is a widely used clustering tool. I…
SparseMix clusters sparse high dimensional binary data efficiently.
problem Clustering sparse high dimensional binary data.
method SparseMix is a mixture model designed for sparse data, using an on-line Hartigan optimization algorithm.
result SparseMix builds partitions with higher compatibility with reference grouping than related methods.
Unified framework for clustering with sparse convex combinations.
problem Challenges in subspace clustering with limited labelled data.
method Spectral-based sparse subspace representation with extensions to constrained and active learning.
result Effective and competitive clustering results on simulated and real data.
New algorithm clusters sparse, high-dimensional texts efficiently.
problem Clustering very short texts with high dimensions and sparsity.
method Linear algebra-based subspace clustering algorithm.
result Algorithm performs competitively on text categorization tasks.
NLSSC improves clustering by enhancing separability in sparse coding.
problem Improving clustering performance in subspace clustering problems.
method Introduces a novel objective term for local separability in non-negative local sparse coding.
result NLSSC outperforms state-of-the-art methods in clustering benchmarks.
Sparse variational posteriors improve clustering and topic modeling speed.
problem Inefficient storage and runtime costs for standard variational posteriors.
method Sparse variational distributions with tunable threshold L L L . result Moderate values of L > 1 L>1 L > 1 provide superior performance in clustering and topic modeling. Improved SSC clustering with reduced computation time and accuracy.
problem Heavy computational burden in Sparse Subspace Clustering.
method RCOMP-SSC algorithm that restricts connections during OMP iterations.
result Improved clustering accuracy with reduced computational time.
VC-PCR improves prediction by clustering correlated variables.
problem Decreased prediction accuracy due to cluster structure in predictor variables.
method Supervised variable selection and clustering to integrate cluster information into a sparse modeling process.
result VC-PCR achieves better prediction, variable selection, and clustering performance.
Spectral clustering with edge counting detects communities in sparse models.
problem Detecting communities in sparse latent space models.
method Spectral clustering followed by edge counting.
result Algorithm achieves consistency and optimality for a broad class of models.
Simple, scalable sparse k-means for high-dimensional data.
problem Clustering in high-dimensional feature spaces with few relevant features.
method Feature ranking-based sparse k-means algorithm.
result Consistent and convergent sparse k-means clustering.
Method decomposes streaming data into sparse and low-rank components from compressive measurements.
problem Online decomposing compressive streaming data efficiently.
method Solves n n n - ℓ 1 \ell_1 ℓ 1 cluster-weighted minimization to decompose sparse and low-rank components. result Outperforms existing methods for numerical and video data.
New algorithm achieves optimal clustering for sparse centers with high dimensions.
problem Statistical and computational limits of clustering sparse centers with high dimensions.
method Sparse clustering algorithm based on sparse PCA.
result Achieves minimax optimal misclustering rate under certain conditions.
The paper uses TDA to select stocks for a sparse portfolio, improving performance across market scenarios.
problem Sparse portfolio selection in financial markets.
method Topological data analysis (TDA) for clustering stock price movements.
result The TDA-based clustering strategy significantly enhances sparse portfolio performance.
Paper proposes an accelerated algorithm for sparse subspace clustering.
problem Inefficient and inaccurate subspace clustering methods.
method Accelerated orthogonal least-squares for sparse subspace clustering.
result The proposed method is more accurate and efficient than existing methods.
Paper connects probability density cuts to graph theory eigenfunctions.
problem Developing sparse cuts for probability densities.
method Defines sparse cuts and principal eigenfunctions for probability densities, proving Cheeger and Buser inequalities.
result No such inequalities hold for prior definitions, proving new inequalities for probability densities.
Proposes TAGnet, a deep model for clustering tasks.
problem Bottlenecks in sparse coding-based clustering methods.
method Emulates sparse coding pipeline using deep learning, introduces TAGnet.
result TAGnet outperforms state-of-the-art methods.
Proposes Lasso Weighted k-means for sparse clustering of high-dimensional data.
problem Sparse clustering of high-dimensional data with variable feature weights.
method Introduces a lasso-based penalty term on feature weights for sparse clustering without distributional assumptions.
result Establishes strong consistency of the algorithm and competitive performance on real and synthetic datasets.
Paper proposes an efficient algorithm for clustering with sparse feature selection.
problem Estimating labels and sparse weights in unsupervised clustering.
method Alternating minimization of Frobenius norm criterion with K-sparse algorithm.
result Significantly improves clustering results on single-cell RNA sequencing datasets.
Proposes ARSK for robust and sparse clustering.
problem Outliers and high-dimensional noisy variables in K-means clustering.
method Introduces redundant error component and group sparse penalty for robustness, and weights and sparsity control penalty for noisy variables.
result Superior performance in identifying clusters without outliers and informative variables.
A new clustering algorithm reduces density peaks clustering's computational complexity.
problem High computational complexity of density peaks clustering.
method Sparse distance matrix, sparse search, K-d tree, second-order difference method.
result Reduced computational complexity from O ( n 2 K ) O(n^2K) O ( n 2 K ) to O ( n ( n 1 − 1 / K + k ) ) O(n(n^{1-1/K}+k)) O ( n ( n 1 − 1/ K + k )) . Improved community detection in sparse graphs using Bethe-Hessian matrix.
problem Community detection in sparse heterogeneous graphs.
method Spectral clustering based on the Bethe-Hessian matrix H r H_r H r for degree-corrected stochastic block models. result Clustering is insensitive to degree heterogeneity for r = ζ r = ζ r = ζ . NMF linked to clustering, reviewed and explained.
problem Linking NMF to clustering.
method Exploring NMF variants and their clustering interpretations.
result NMF variants can be used for clustering.
DADC algorithm improves clustering for data with varying density.
problem Sparse cluster loss and cluster fragmentation in density peak clustering.
method Domain-adaptive density measurement, cluster center self-identification, and cluster self-ensemble.
result DADC achieves more reasonable clustering results on data with varying density.
Efficiently clusters noisy signals using structured sparsity and wavelet transforms.
problem Clustering signals with low Signal-to-Noise Ratio (SNR).
method Sparse K-means algorithm with structured sparsity, wavelet multi-scale property, and scattering transform.
result Improved clustering results on real datasets.
In many real-world problems, we are dealing with collections of high-dimensional data, such as images, videos, text and web documents, DNA microarray data, and more. Often, high-dimensional data lie close to low-dimensional structures corresponding to several classes or categories the data belongs to. In this paper, we…
Unified spectral clustering for sparse networks with heterogeneous degrees.
problem Efficiently detecting communities in sparse networks with varying degrees.
method Developed a parametrized regularized Laplacian matrix for spectral clustering.
result Improved parametrization accounts for network heterogeneity and community hardness.
New method discovers concepts in hidden feature layers using sparse subspace clustering.
problem Local attribution methods fail to identify coherent model behavior across samples.
method Sparse Subspace Clustering (SSCC) for concept discovery.
result Empirically validated method for various image classification tasks.
A faster Wasserstein k-means algorithm for histogram data reduces computation and maintains clustering quality.
problem Efficiently clustering histogram data with reduced computation time.
method Sparse simplex projection to reduce data samples, centroids, and ground cost matrix, dynamically removing lower-valued samples.
result Significant reduction in computational complexity without compromising clustering quality.
Proposes a semi-supervised K-Means algorithm for better feature selection.
problem Data clustering with unknown feature quality and limited labelled data.
method Combines unsupervised sparse clustering and semi-supervised learning with labelled data.
result The algorithm identifies informative features and maintains high performance.
New method clusters multi-view data by squeezing hybrid knowledge.
problem Removal of redundant information and fusion of multi-view features.
method Low-rank subspace multi-view clustering with adaptive graph regularization.
result Our method outperforms state-of-the-art algorithms on multi-view benchmarks.
The paper proposes a new method for clustering high-dimensional data.
problem Clustering high-dimensional data with separation of clustered and noise variables.
method Discriminative clustering with sparse regularizers, convex relaxations, and efficient iterative algorithm.
result The proposed method achieves scalings of d = O ( n ) d=O(\sqrt{n}) d = O ( n ) for the affine invariant case and d = O ( n ) d=O(n) d = O ( n ) for the sparse case. Paper proposes a new method for sparse spectral clustering on Stiefel manifold.
problem Sparse spectral clustering on Stiefel manifold with nonsmooth and nonconvex objective.
method Proposes a manifold proximal linear method (ManPL) to solve the original SSC formulation.
result Demonstrates the advantage of ManPL over existing methods on single-cell RNA sequencing data.
Sparse elasticity reconstruction from local displacements reduces error.
problem Reconstructing elasticity from limited data.
method Sparse elasticity reconstruction theory, local clustering, alternating optimization.
result Higher spatial resolution elasticity distribution estimation.
A new clustering method for simplicial complexes using homology.
problem Clustering simplicial complexes efficiently and accurately.
method Inspired by graph spectral clustering, the method uses sparse eigenproblems.
result Produces clusters sensitive to simplicial complex homology.
New method speeds up subspace clustering by 20-30x.
problem Efficiently clustering high-dimensional data with correlated variables.
method Modified sparse subspace clustering using Ordered Weighted ℓ 1 \ell_1 ℓ 1 (OWL) regression. result Significantly reduces computational complexity and achieves better clustering results.