A new framework learns shared features from multi-view data with many-to-many associations.
problem Learning shared features from multi-view data with many-to-many associations.
method Probabilistic Multi-view Graph Embedding (PMvGE) using neural networks.
result PMvGE outperforms existing multi-view methods in large-scale datasets.
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.
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.
This paper proposes a multi-view learning framework for dimension reduction.
problem Challenges in learning low-dimensional spaces from multiple heterogeneous features.
method Similarity-consensus regularized multi-view learning framework.
result The proposed method achieves comparable performance to previous multi-view learning approaches.
Gen-RKM framework generates multi-view data with uncorrelated features.
problem Joint multi-view generation and disentangled feature learning.
method Joint multi-view generation using shared representation; primal and dual formulations; novel training procedure for neural networks.
result Joint multi-view generation and uncorrelated feature learning achieved.
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.
Proposes a method to learn from multiple views with low-rank embeddings.
problem Learning from multiple views with varying correlations is challenging.
method Multi-view Locality Low-rank Embedding (MvL2E) method that uses low-rank representations and centroid-based scheme.
result MvL2E achieves comparable performance with previous methods on benchmark datasets.
Proposes a multi-view DR algorithm to improve learning performance.
problem Improving learning performance with multi-view high-dimensional data.
method Smooth Preserve Projection extended to multi-view using Hilbert-Schmidt Independence Criterion.
result Excellent performance on multi-view datasets.
Proposes KMvDA for object recognition from multi-view data.
problem Recognizing objects from different views, even when views are heterogeneous.
method Introduces kernel multi-view discriminant analysis (KMvDA) and uses random Fourier features (RFF) for large-scale learning.
result KMvDA and RFF approximation improve object recognition from multi-view data.
Proposes MvLPE for better multi-view representation learning.
problem Learning representations from multi-view data with varying correlations.
method Integrates multi-view data into a centroid view while maintaining low-rank reconstruction relations.
result MvLPE outperforms existing methods on benchmark datasets.
PROTOCOL tackles imbalanced multi-view clustering by enhancing contrastive learning.
problem Class imbalance in real-world multi-view data.
method PROTOCOL uses partial optimal transport to perceive and mitigate imbalance, enhancing contrastive learning.
result PROTOCOL significantly improves clustering performance on imbalanced multi-view data.
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.
Proposes a method to learn rankings from multiple sources.
problem Learning to rank from multiple information sources.
method Generic framework for multi-view subspace learning to rank (MvSL2R) with autoencoder-like networks and end-to-end solutions.
result Enhances joint ranking with minimum view-specific ranking loss, achieving maximum global view agreements.
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.
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.
Adaptive kernel model for multi-view learning reduces overfitting and scales with data.
problem Overfitting and high computational cost in multi-view learning.
method Bayesian framework, Dirichlet process Gaussian mixtures, random Fourier features, max-margin constraints, MCMC sampler.
result Adaptive learning of shift-invariant kernels from data.
Enhances GPLVM for multi-view data with scalable latent representation learning.
problem Limited kernel expressiveness and computational inefficiency in multi-view GPLVM.
method Introduces a new duality between spectral density and kernel function, uses NG-SM kernel, and applies random Fourier feature approximation for scalability.
result Consistently outperforms state-of-the-art models in learning meaningful latent representations across diverse datasets.
Proposes a method for multi-view clustering that considers local structures and feature weights.
problem Challenges in effectively exploiting complementary information across multiple views.
method Simultaneously assigns weights to different features and captures local information in view-specific feature spaces.
result Achieves state-of-the-art performance on benchmark datasets.
Enhances robustness of multi-view clustering via partition fusion.
problem Dealing with noises and inconsistency in multi-view data.
method Generates multiple partitions, integrates them, and co-evolves graph learning, partition generation, and view weight learning.
result Empirical results verify the effectiveness and robustness of the proposed approach.
DEMVC improves multi-view clustering with collaborative training and deep autoencoders.
problem Existing multi-view clustering methods have high computation and space complexities or lack representation capability.
method DEMVC learns embedded representations of multiple views individually using deep autoencoders and collaboratively trains all views.
result DEMVC achieves significant improvements over state-of-the-art methods on multi-view datasets.
Enhances hashing for cross-modal retrieval using multi-view features.
problem Limited improvement in single-view hashing for cross-modal retrieval.
method Exploits multiple views to enrich feature information, learning discriminative hash codes.
result Superior performance compared to state-of-the-art methods on various datasets.
Novel method for multi-view metric learning in vector-valued kernel spaces.
problem Metric learning for multi-view data with multi-modal structure.
method Convex optimization problems and iterative multi-view metric learning algorithm with Nyström approximation.
result Improved performance on real-world datasets compared to state-of-the-art methods.
Proposes a self-boosted time series forecasting model using multi-task and multi-view learning.
problem Improving robustness and performance of time series forecasting models.
method Decomposes original time series into multiple series, uses multi-task learning for closely related series, and multi-view learning for loosely related series.
result Shows superior performance compared to existing methods on real-world datasets.
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.
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.
Sparse coding is a common approach to learning local features for object recognition. Recently, there has been an increasing interest in learning features from spatio-temporal, binocular, or other multi-observation data, where the goal is to encode the relationship between images rather than the content of a single ima…
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.
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.
Proposes LMSSC for multi-view semi-supervised classification.
problem Leveraging multiple complementary views for improved classification.
method Semi-supervised classification with latent multi-view representation learning.
result Unified framework for latent representation learning, graph construction, and label propagation.
Mixup helps learn both features in multi-view classification problems.
problem Learning both features in multi-view classification problems.
method Training with a specific instantiation of Mixup.
result Training with Mixup leads to learning both features for every class.
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.
Unified multi-view learning framework using OPLS with regularization and deep extensions.
problem Improving multi-view learning for classification and feature extraction.
method Orthonormalized Partial Least Squares (OPLS) with regularization and deep extensions.
result Unified multi-view learning framework with improved performance.
A new method detects and ranks anomalies in cloud computing platforms using multi-view learning.
problem High false-alarm rate in traditional anomaly detection methods.
method Online model using machine learning theory, ELM for efficiency, and multi-view feature fusion.
result Improved accuracy and efficiency in anomaly detection and ranking.
DMFAW improves multi-view clustering with adaptive weights and feature selection.
problem Lack of effective feature selection and empirical hyperparameter selection in existing deep matrix factorization methods.
method Introduces Deep Matrix Factorization with Adaptive Weights (DMFAW) for multi-view clustering, incorporating feature selection and dynamically updating weights using Control Theory.
result DMFAW outperforms state-of-the-art methods in clustering performance.
Proposes Gromov-Wasserstein methods for multi-view embedding.
problem Integrating multiple representations of the same samples in heterogeneous geometries.
method Gromov-Wasserstein optimal transport for multi-view embedding.
result Preserves intrinsic relational structure across views effectively.
Paper proposes a model to integrate diverse drug features for accurate similarity measures.
problem Challenges in integrating heterogeneous, noisy, nonlinear-related drug features.
method Attentive Multi-view Graph Auto-Encoders with flexible design for semi-supervised and unsupervised settings.
result Significant predictive accuracy improvement and better interpretability.
Proposes a method to improve reinforcement learning in multi-view environments.
problem Improving policy learning in environments with partial observability.
method Actor-Critic-Attention Mechanism for generating a single feature representation from multiple views.
result Our method outperforms state-of-the-art baselines on various complex 3D environments.
Proposes CRG_IMSC for better clustering of multi-view data.
problem Lack of effective connectivity in clustering results.
method Directly obtains clustering result with nonnegative constraint; constructs connectivity matrix based on spectral clustering result; uses multiplicative update algorithm.
result Improves clustering performance on benchmark datasets.
Develops a feature selection method for multi-view data with mixed types.
problem Challenges in feature selection for high-dimensional multi-view data with mixed data types.
method Block Randomized Adaptive Iterative Lasso (B-RAIL) combining randomized Lasso, adaptive weighting, and stability selection.
result Demonstrates effectiveness of B-RAIL in identifying biomarkers and novel candidates for ovarian cancer.
iGecco+ integrates multi-view data for better clustering.
problem Discovering common group structure in mixed multi-view data.
method Integrative Generalized Convex Clustering Optimization (iGecco) with adaptive feature selection.
result iGecco+ achieves superior clustering performance on high-dimensional mixed multi-view data.
Fine-grained pretraining improves neural network's ability to learn rare features.
problem Improving generalization in deep learning models.
method Introducing a hierarchical multi-view structure to confine input data distribution.
result Fine-grained pretraining leads to better accuracy on hard downstream test samples.
New methods for visualizing multi-view data improve clustering accuracy.
problem Visualizing multi-view data for better understanding of samples.
method Extensions of manifold learning methods (t-SNE, LLE, ISOMAP) for multi-view data.
result Improved clustering accuracy of multi-view data.
A new method integrates multi-label and multi-view features for image classification.
problem Combining multi-label and multi-view information for effective image classification.
method Introduces MV3MR, a method that exploits the complementary property of different features and discovers intrinsic local geometry. result MV3MR outperforms existing methods on PASCAL VOC' 07 and MIR Flickr datasets. Proposes a new method for joint sample and feature selection in multi-view data.
problem Cannot detect latent subsets of samples and remove outliers.
method Weighted Sparse Partial Least Squares (ℓ∞/ℓ0-wsPLS) method for joint sample and feature selection. result Developed globally convergent algorithm and iterative algorithms for multi-view data fusion.
New method discovers useful structure in multi-view data for clinical applications.
problem Detecting slow bleeding in patients monitored for central venous pressure.
method Proposes a method to characterize globally nonlinear multi-view relationships using a mixture of linear relationships.
result Demonstrates the potential to find useful structure in data that is hard to find with single-view or current multi-view methods.
MPWTSVM improves multi-view learning by reducing redundancy and enhancing accuracy.
problem Improving multi-view learning models for better accuracy and efficiency.
method Proposes MPWTSVM, which combines WLTSVM's strengths with multi-view learning principles.
result Demonstrates better accuracy and efficiency compared to existing multi-view classification models.
Proposes a novel method to identify complex effects in multi-view datasets.
problem Challenges in analyzing multi-view biomedical datasets with complex interactions.
method Generalized kernel machine approach considering marginal and joint effects of features from different views.
result Effective identification of higher-order composite effects in multi-view datasets.
scICML integrates multi-omics data from single cells using co-clustering.
problem High noise and sparsity in multi-omics data from single cells.
method Information-theoretic co-clustering-based multi-view learning.
result Improves clustering performance and provides biological insights.