Error-robust multi-view clustering tackles noisy data across multiple sources.
problem Error in multi-view data degrades clustering performance.
method Blind clustering without error consideration is ineffective. Various approaches like sparsity, graph, subspace, and deep learning are reviewed.
result Error-robust multi-view clustering improves clustering accuracy even with corrupted data.
Proposes VIGAN for imputing missing views in multi-view datasets.
problem Missing view problem in multi-view datasets.
method Generative Adversarial Networks (GANs) and Multi-modal Denoising Autoencoder (DAE).
result VIGAN effectively recovers missing views from paired data across views.
Algorithm fine-tunes multi-view weights for streaming data.
problem Streaming data in multi-view learning.
method Fine-tuning combination weights of stable subspaces.
result Algorithm effectively handles streaming views.
A novel method for clustering multi-view data using dual representations.
problem Clustering multi-view data with consistent and unique information.
method One-step multi-view clustering method exploiting dual representations.
result The proposed method improves clustering performance on benchmark datasets.
Paper proposes AMSRE for multi-view data reduction.
problem Enhance performances of multi-view data tasks.
method Auto-weighted Multi-view Sparse Reconstructive Embedding (AMSRE).
result AMSRE effectively reduces multi-view data dimensions.
FCMSC combines multi-view data through feature concatenation for improved clustering.
problem Clustering multi-view data with diverse and sometimes incompatible views.
method FCMSC concatenates multi-view data, integrates l2,1-norm, and uses graph regularization to explore consensus and complementary information. result FCMSC outperforms state-of-the-art multi-view clustering methods on six real-world datasets.
ANN clusters multi-view data by agglomerating subviews and avoiding postprocessing.
problem Challenges in multi-view clustering, especially subview agglomeration.
method Agglomerative Neural Network (ANN) using Constrained Laplacian Rank.
result ANN outperforms state-of-the-art methods in multi-view clustering.
Semi-supervised model learns multi-view data representations.
problem Learning discriminative representations with limited labeled data.
method Multi-view Discriminative Neural Network (MDNN) with coupled deep networks.
result MDNN effectively separates and clusters samples from different and same classes.
Proposes D-CDLF for multi-view data decomposition.
problem Uncorrelatedness between common and distinctive latent factors.
method Decomposes data into common, distinctive, and noise components.
result Effective uncorrelatedness between distinctive latent factors from different views.
New method for clustering large multi-view data.
problem Handling large multi-view data efficiently.
method Incremental minimax optimization based fuzzy clustering (IminimaxFCM).
result IminimaxFCM outperforms related methods in clustering accuracy.
A neural network combines multi-view mammography data for breast cancer classification.
problem Classifying breast microcalcifications as benign or malignant using multiple imaging views.
method Mixture of Views network architecture for multi-view data fusion.
result The method outperforms existing fusion methods in classifying breast microcalcifications.
Proposes MV-Co-VH for multi-view clustering using visible and hidden views.
problem Lack of efficient algorithms for fully utilizing multi-view data.
method Projects multiple views to a common hidden space using NMF, then applies collaborative learning.
result Competitive clustering performance on UCI and real-world datasets.
This paper tackles incomplete multi-view clustering with spectral perturbation theory.
problem Realistic clustering scenario where data instances are missing in certain views.
method Spectral perturbation theory and matrix completion method for incomplete similarity matrix.
result The minimization of perturbation risk bounds maximizes the final fusion result across all views.
Proposes CVKT to complete missing kernel matrices across multiple views.
problem Missing data in multiple views of kernel matrices.
method Cross-View Kernel Transfer (CVKT) with kernel alignment.
result Predicts missing values in kernel matrices using other views' data.
New method for imputing missing data in multi-view data.
problem Large missing data sets in multi-view data.
method Stacked penalized logistic regression (StaPLR) in a dimension-reduced space.
result New method performs competitively with lower computational cost.
Proposes MinimaxFCM for multi-view clustering of data from multiple sources.
problem Clustering data from multiple heterogeneous views.
method Minimax optimization-based fuzzy c means clustering.
result MinimaxFCM outperforms other multi-view clustering methods.
Proposes MVMC for multi-view clustering, enhancing diversity and quality.
problem Leveraging multi-view data for diverse clustering.
method Adapts multi-view self-representation learning, HSIC for redundancy reduction, matrix factorization.
result Generates multiple high-quality and diverse clusterings from multi-view data.
S-multi-SNE combines t-SNE with labelling data for better multi-view data visualisation and classification.
problem Visualizing and classifying multi-view data with limited labels.
method S-multi-SNE extends multi-SNE by incorporating labelling data as an additional view for semi-supervised classification and visualization.
result Including labelling information significantly improves the projection of samples and classification performance.
Federated multi-view matrix factorization learns from multiple data sources without centralizing user data.
problem Cold-start federated recommendations and multi-view data structure.
method Federated learning framework extended to multi-view matrix factorization.
result Federated multi-view matrix factorization outperforms simpler methods in cold-start federated recommendations.
Proposes a method to enhance multi-view learning by maximizing higher order correlations.
problem Losing intrinsic interconnections among multiple views in pairwise correlation maximization.
method Formulates multi-view data as a low rank approximation problem using higher order correlation tensor and solves it with the generating polynomial method.
result Consistently outperforms prior methods on real multi-view data.
StaPLR selects important views for multi-view learning models.
problem Selecting important views in multi-view learning to reduce data collection costs.
method Developed stacked penalized logistic regression (StaPLR) for view selection.
result StaPLR outperforms existing methods in view selection, reducing false positives.
MAD learns alignments between data points from multiple views.
problem Finding correspondences across different data views.
method Probabilistic model for learning alignments between data points and views.
result MAD can recover a global alignment from a few aligned examples.
Finding relationships between multiple views of data is essential both for exploratory analysis and as pre-processing for predictive tasks. A prominent approach is to apply variants of Canonical Correlation Analysis (CCA), a classical method seeking correlated components between views. The basic CCA is restricted to ma…
DMClusts discovers multiple clusterings from multi-view data.
problem Finding multiple meaningful and diverse clusterings from multi-view data.
method Deep matrix factorization to gradually factorize multi-view data into representational subspaces and generate one clustering per layer, enforcing diversity through proximity minimization.
result DMClusts outperforms state-of-the-art multiple clustering solutions.
Proposes WM-NMF for better multi-view clustering.
problem Learning multi-view data with unequal view information content.
method Introduces a weighted multi-view NMF algorithm to learn view-specific and observation-specific weights.
result Achieves better clustering performance and handles noisy data.
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.
Develops a new method to analyze hierarchical data from multiple perspectives.
problem Current multi-view and hierarchical methods are limited and do not apply to graphical data.
method Generalizes Treelet Transform to Multi-View Treelet Transform (MVTT).
result MVTT captures hierarchical structure in multi-view settings.
New method models multi-view noise as Mixture of Gaussians for robust MSL.
problem Robust multi-view subspace learning with complex noise.
method Model multi-view noise as Mixture of Gaussians, regularize across views.
result Method outperforms existing models in robustness and interpretability.
This paper improves multi-view spectral clustering by preserving local manifold structures.
problem Agreeing on data object grouping across multiple views with noisy data.
method Iterative Low-Rank based Structured Optimization method using multi-graph Laplacians.
result Improved spectral clustering by preserving local manifold structures.
This paper surveys multi-view clustering methods and proposes a taxonomy.
problem The challenge of combining multiple data views for clustering.
method Review of existing multi-view clustering methods and taxonomy proposal.
result A novel taxonomy of multi-view clustering methods is proposed.
Proposes a method for two-sided clustering of co-occurrence data.
problem Efficient clustering of co-occurrence data in multi-view settings.
method Information-theoretic multi-view co-clustering (MV-ITCC).
result Demonstrates superior performance on text and image datasets.
A new clustering method learns shared hidden space and fuzzy partition between multi-view data.
problem Effective exploitation of relationship between different views in multi-view data.
method Hidden space sharing multi-view fuzzy clustering (HSS-MVFC) method based on fuzzy c-means.
result The proposed method outperforms many related clustering methods in experiments.
Proposes JACA for joint analysis of multi-view data with class information.
problem Finding associations between multi-view data related to class memberships.
method Joint Association and Classification Analysis (JACA) framework.
result Improved misclassification rates and stronger associations compared to existing methods.
Proposes CI-GMVC to improve graph-based multi-view clustering performance.
problem Inconsistency in multi-view data affects clustering performance.
method Integrates consistent and inconsistent parts of multiple views using a unified matrix.
result Demonstrates improved clustering performance on real-world datasets.
New methods integrate nonlinear, sparse, and multi-view aspects for high-dimensional data analysis.
problem Integrating nonlinear dependence, sparsity, and multi-view data in high-dimensional datasets.
method Proposes HSIC-SGCCA, SA-KGCCA, and TS-KGCCA methods for multi-view high-dimensional data analysis.
result HSIC-SGCCA outperforms competing methods in multi-view variable selection.
New methods discover causal relationships from multiple related data views.
problem Causal discovery from non-Gaussian data.
method Multi-view linear Structural Equation Model (SEM) with weak assumptions.
result Identifiability of acyclic SEMs and successful causal graph estimation.
This paper presents eight PAC-Bayes bounds to analyze the generalization performance of multi-view classifiers. These bounds adopt data dependent Gaussian priors which emphasize classifiers with high view agreements. The center of the prior for the first two bounds is the origin, while the center of the prior for the t…
A new method for cross-view classification using divide-and-conquer.
problem Cross-view classification with nonlinear manifolds and outliers.
method Divide-and-Conquer strategy applied to three subproblems: view discrepancy, intrinsic structure, and discriminability.
result Significant improvement in classification accuracy and robustness compared to state-of-the-art methods.
R package `mvs` handles multi-view data for better model performance.
problem Classical machine learning ignores multi-view data structure, limiting interpretability and performance.
method Multi-view stacking (MVS) framework, training on each view separately, cross-validation for weights, ensemble learning.
result MVS improves model performance on multi-view data.
CAMul forecasts with calibrated and accurate multi-view time-series data.
problem Combining diverse data sources for reliable time-series forecasting.
method CAMul integrates multi-modal data views dynamically, assigning importance based on context.
result CAMul outperforms state-of-the-art models by 25% in accuracy and calibration.
New method clusters strong and weak views effectively, improving performance by up to 40%.
problem Clustering incomplete multi-view data with unbalanced incompleteness.
method View evolution scheme and weighted multi-view subspace clustering.
result Improves clustering performance by up to 40% on three metrics.
Proposes a multi-view VAE for imputing missing data from correlated sources.
problem Imputing missing data from multi-view sources with latent space correlation.
method Enforces a joint prior with latent space correlation between VAEs trained on each view.
result More strongly correlated latent spaces are uncovered, enabling effective imputation.
A new method for clustering multi-view data with missing views.
problem Clustering multi-view datasets with missing views is challenging.
method OPIMC framework using regularized and weighted matrix factorization.
result OPIMC efficiently clusters multi-view datasets with missing views.
Contrastive learning works well with redundant data views.
problem Improving unsupervised learning with redundant data views.
method Theoretical analysis of contrastive learning in a multi-view setting.
result Linear functions of learned representations are nearly optimal on prediction tasks when views are redundant.
Unified framework for multi-view autoencoders simplifies model implementation and evaluation.
problem Inconsistent notation and implementation of multi-view autoencoders.
method Unified mathematical framework, documentation, and Python library.
result Comparable or superior performance in benchmarking experiments.
This paper tackles unpaired data in multi-view learning, proposing a new framework and models.
problem Handling unpaired data in multi-view learning, which is more common than paired data.
method Generalized uncorrelated multi-view subspace learning framework with successive alternating approximation (SAA) method.
result Proposed models perform competitively or better than baselines in multi-view feature extraction and multi-modality classification.
CLIM-FS tackles mixed-missing multi-view unsupervised feature selection.
problem Mixed-missing multi-view data with incomplete features and views.
method Integrates imputation of missing views and variables into feature selection model based on nonnegative orthogonal matrix factorization.
result CLIM-FS outperforms state-of-the-art methods on real-world datasets.
A novel multi-view spectral clustering model fuses and clusters data views.
problem Fusing and clustering multi-view data effectively.
method Simultaneously fuses and clusters views into a single graph.
result The proposed method outperforms existing techniques.