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.
Method captures shared information across many views robustly.
problem Modeling hundreds of views per event and learning robust embeddings without view knowledge.
method View bootstrapping using multi-view correlation and matrix concentration theory.
result View bootstrapping captures shared information across many views robustly.
Traditional multi-view learning approaches suffer in the presence of view disagreement,i.e., when samples in each view do not belong to the same class due to view corruption, occlusion or other noise processes. In this paper we present a multi-view learning approach that uses a conditional entropy criterion to detect v…
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.
Poly-view contrastive learning improves image representation learning.
problem Learning representations from multiple related views in image data.
method Developed new representation learning objectives for poly-view tasks using information maximization and sufficient statistics.
result Poly-view contrastive models trained for fewer epochs and with smaller batch sizes outperform models trained for more epochs and with larger batch sizes.
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.
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.
This paper proposes a multi-view representation learning approach for robust node representations.
problem Learning robust node representations across multiple types of network views.
method Promotes collaboration between different views using an attention mechanism.
result The proposed approach outperforms existing methods for network representation learning.
Proposes HOT method for robust multi-view learning.
problem Inability of traditional methods to handle unaligned and non-distributionally aligned views.
method Hierarchical optimal transport (HOT) method that penalizes sliced Wasserstein distances between different views.
result HOT method achieves robust performance on both synthetic and real-world tasks.
New GP model for multi-view learning combining views with regularization.
problem Lack of multi-view learning applications in Gaussian processes.
method Regularizes marginal likelihood with consistency among latent functions from different views, and improves model with point selection scheme.
result Improves performance in multi-view learning across multiple real-world datasets.
Proposes a new multi-view graph learning framework to model consistency and inconsistency.
problem Graph learning methods often neglect inconsistency across multiple views, making them vulnerable to noisy datasets.
method Proposes a unified objective function to simultaneously model consistency and inconsistency, iteratively learning consistent and unified graphs.
result Demonstrates robustness and efficiency of the proposed approach on twelve multi-view datasets.
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.
Unified multi-view sentence representation improves downstream tasks.
problem Improving sentence representation learning from diverse views.
method Unified multi-view sentence representation learning framework using RNN and linear model, maximizing agreement with adjacent context.
result Improved representations and transferability on downstream tasks.
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.
End-to-end method learns geometry and appearance for multi-view object detection.
problem Challenges in multi-view object detection, including viewpoint, lighting, and scale variability.
method Jointly learns multi-view geometry and warping for robust cross-view object detection.
result Superior performance compared to baselines on a new street-level panorama data set.
FAML addresses biased evidence learning in multi-view learning, improving fairness and prediction reliability.
problem Biased evidence learning in multi-view learning, leading to unreliable uncertainty estimation.
method FAML introduces an adaptive prior and fairness constraint to balance evidence allocation across views.
result FAML enhances fairness and improves prediction reliability compared to state-of-the-art methods.
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.
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.
A new method learns to prioritize and use multiple views of an environment for better decision-making.
problem Learning from multiple views of an environment to improve decision-making.
method Attention-based deep reinforcement learning to dynamically attend to views of the environment.
result The method improves performance in complex 3D environments with obstacles.
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.
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 a deep probabilistic multi-view model for multi-view learning.
problem Learning from multiple related views with shared latent structure.
method Probabilistic Canonical Correlation Analysis (CCA) in latent space, deep generative networks, variational inference.
result Efficient variational inference approximates posterior distributions of latent multi-view layer.
A new approach to learn weights for multi-view clustering.
problem Measuring the importance of each view in multi-view clustering.
method Proposes a re-weighted approach to learn intrinsic weights for multi-view clustering.
result The proposed approach is effective and practical for multi-view clustering.
Study improves deep learning chest X-ray models by incorporating lateral views.
problem Lack of lateral views in training datasets limits deep learning performance.
method Used PadChest dataset with multiple views to explore merging methods.
result Incorporating lateral views increases model performance for 32 labels.
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.
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.
Dual memory model learns from two asynchronous views.
problem Capturing long-term intra-view and inter-view interactions in multi-view sequential data.
method Two encoders for reading and writing to external memories, late-fusion and early-fusion modes of memory accessing.
result Competitive performance on synthetic and healthcare tasks.
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.
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.
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 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.
A novel method integrates feature and topology views for unsupervised graph representation learning.
problem Lack of mutual information across feature and topology views in graph representation learning.
method Proposes a multi-view representation learning module and a common representation learning module using mutual information maximization and reconstruction loss minimization.
result Demonstrates effectiveness in integrating feature and topology views, achieving comparable or better performance than supervised methods.
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.
This paper analyzes multi-view learning using information theory to improve generalization.
problem Lack of theoretical understanding of multi-view learning's generalization behavior.
method Developed information-theoretic generalization bounds for multi-view learning.
result Capturing both consensus and complementary information maximizes representation disentanglement.
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.
Unified framework for multi-view learning with orthogonal projections.
problem Learning individual orthogonal projections for multiple views.
method Successive approximations via eigenvectors, iterative Krylov subspace method.
result Consistently competitive and often better than existing methods.
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.
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.
A boosting algorithm PB-MVBoost learns weights for view-specific classifiers and views.
problem Improving multiview learning by balancing accuracy and diversity.
method Iteratively learns weights for view-specific classifiers and views using a PAC-Bayes multiview C-Bound.
result Efficiency demonstrated on three publicly available datasets.
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.
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.
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.
Similarity between objects is multi-faceted and it can be easier for human annotators to measure it when the focus is on a specific aspect. We consider the problem of mapping objects into view-specific embeddings where the distance between them is consistent with the similarity comparisons of the form "from the t-th vi…
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.
A new framework decouples SSL tasks into VDA and VLC, revealing VDA's importance.
problem Designing effective self-supervised learning tasks without manual annotation.
method Borrowing a multi-view perspective, the paper decouples popular pretext tasks into VDA and VLC, focusing on VDA's role in feature learning.
result VDA tasks dominate SSL performance, and integrating predictions from augmented views improves overall performance.
Paper tackles multi-view reinforcement learning with two methods.
problem Decision making with shared dynamics and different observation models.
method Observation augmentation and cross-view policy transfer.
result Reductions in sample complexities and computational time for multi-view environments.
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.
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.