Framework for incomplete multi-view learning improves efficiency and clustering accuracy.
problem Incomplete representation in multi-view data.
method Joint Embedding Learning and Low-Rank Approximation (JELLA) framework.
result Improves efficiency and clustering accuracy in incomplete multi-view data.
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.
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.
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.
HSACC improves multi-view clustering of incomplete data.
problem Challenges in clustering incomplete multi-view data.
method Hierarchical Semantic Alignment and Cooperative Completion framework.
result HSACC outperforms state-of-the-art methods on benchmark datasets.
A new algorithm tackles incomplete multi-view clustering.
problem Incomplete multi-view clustering where views have missing instances.
method DAIMC algorithm using weighted semi-NMF and L2,1-Norm regularized regression. result DAIMC effectively handles missing instances and improves clustering accuracy.
Framework for generating multiple clusterings from multi-view data.
problem Challenges in finding optimal clustering criteria and handling incomplete multi-view data.
method DiMVMC framework that optimizes multiple decoder deep networks to complete data views and generate shared representations.
result DiMVMC outperforms state-of-the-art competitors in generating multiple clusterings with high diversity and quality.
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.
Merlin improves robustness of MTSF models to missing data.
problem Suboptimal forecasting performance due to unfixed missing rates in MTSF models.
method Offline knowledge distillation and multi-view contrastive learning.
result Merlin enhances robustness of MTSF models while preserving accuracy.
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 significant challenge to make learning techniques more suitable for general purpose use is to move beyond i) complete supervision, ii) low dimensional data, iii) a single task and single view per instance. Solving these challenges allows working with "Big Data" problems that are typically high dimensional with multip…
Multi-view data have been routinely collected in various fields of science and engineering. A general problem is to study the predictive association between multivariate responses and multi-view predictor sets, all of which can be of high dimensionality. It is likely that only a few views are relevant to prediction, an…
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.
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 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.
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.
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 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.
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.
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.
With advances in information acquisition technologies, multi-view data become ubiquitous. Multi-view learning has thus become more and more popular in machine learning and data mining fields. Multi-view unsupervised or semi-supervised learning, such as co-training, co-regularization has gained considerable attention. A…
This paper proposes multi-view attack strategies for deep models.
problem Vulnerability of multi-view deep models to adversarial attacks.
method Two multi-view attack strategies: two-stage attack (TSA) and end-to-end attack (ETEA).
result Proposed multi-view attack strategies are effective on multi-view deep models.
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.
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.
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.
OpenViewer tackles multi-view learning challenges with interpretability and generalization.
problem Lack of interpretability and insufficient generalization in multi-view learning models.
method OpenViewer introduces a Pseudo-Unknown Sample Generation Mechanism, Expression-Enhanced Deep Unfolding Network, and Perception-Augmented Open-Set Training Regime.
result OpenViewer effectively addresses openness challenges and enhances recognition performance for both known and unknown samples.
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.
New PAC-Bayesian bounds for multi-view learning using Rényi divergence.
problem Applying PAC-Bayesian theory to multi-view learning.
method Introducing novel PAC-Bayesian bounds based on Rényi divergence for multi-view learning.
result Efficient optimization algorithms that align with theoretical bounds.
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 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 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.
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.
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.
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.
While neural networks for learning representation of multi-view data have been previously proposed as one of the state-of-the-art multi-view dimension reduction techniques, how to make the representation discriminative with only a small amount of labeled data is not well-studied. We introduce a semi-supervised neural n…
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.
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.
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.
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.
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.
Paper introduces new PAC-Bayesian bounds for multi-view domain adaptation.
problem Lack of attention to multi-view learning in domain adaptation.
method Adapted distance measure for multi-view domain adaptation using Pac-Bayesian theory.
result Introduced novel Pac-Bayesian bounds for multi-view domain adaptation.
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.
A simple framework Probabilistic Multi-view Graph Embedding (PMvGE) is proposed for multi-view feature learning with many-to-many associations so that it generalizes various existing multi-view methods. PMvGE is a probabilistic model for predicting new associations via graph embedding of the nodes of data vectors with …
We propose a new approach, multi-view Laplacian support vector machines (SVMs), for semi-supervised learning under the multi-view scenario. It integrates manifold regularization and multi-view regularization into the usual formulation of SVMs and is a natural extension of SVMs from supervised learning to multi-view sem…
Proposes a novel framework for citywide traffic volume inference using GPS and camera data.
problem Inaccurate traffic volume inference due to sparse GPS data and dynamic traffic conditions.
method Combines GPS and camera data, uses a simulator and reinforcement learning to recover missing data, constructs spatiotemporal graphs for inference.
result Effective citywide traffic volume inference validated by experiments.
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 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.