Proposes GRAB-MDM for robust multiview data fusion.
problem Limited theoretical guarantees for multiview fusion methods in noisy high-dimensional data.
method Generalized Robust Adaptive-Bandwidth Multiview Diffusion Maps (GRAB-MDM) with adaptive bandwidth selection.
result Adaptive bandwidths lead to robust recovery of shared intrinsic structure in noisy multiview data.
New theory for curve-based 3D reconstruction and camera estimation.
problem Challenges in point feature extraction limit traditional methods.
method Differential geometry of general curves for image and space curves.
result Theoretical foundations for curve-based multiview reconstruction.
New nonconvex approach for multiview learning improves efficiency and scalability.
problem Efficiently solve multiview representation learning problems.
method Developed a nonconvex formulation and used SGD for efficient solution.
result Nonconvex approach converges to global optima with simple SGD.
NAM learns multiview representations with attention for better task performance.
problem Finding optimal combination of multiviews for specific tasks.
method Introduces Neural Attentive Multiview machine with attention mechanism.
result NAM outperforms single view and alternative multiview methods in item recommendations.
PAC-Bayesian analysis improves multiview learning by controlling diversity and accuracy trade-off.
problem Improving multiview learning by combining classifiers from multiple views.
method PAC-Bayesian framework for a two-step hierarchical multiview learning approach.
result Generalization bound on the risk of the majority vote with a diversity term in predictions.
A new model for multiview data analysis using graph autoencoders.
problem Nonlinear multiview canonical correlation analysis for large datasets.
method Variational approach with graph convolutional neural networks.
result Competitive performance on classification, clustering, and recommendation tasks.
mvlearn simplifies multiview machine learning for non-specialists.
problem Handling multiview data sets from disparate sources.
method Python library implementing multiview machine learning methods.
result Enables non-specialists to use multiview machine learning methods easily.
Paper extends CCA for multiview learning, improving performance.
problem Learning representations across multiple data views.
method Extends CCA to a multiview mixture model with heuristics.
result Improves performance on downstream tasks compared to standard CCA.
Study uses multiview techniques to improve disease classification from health data.
problem Improving disease prediction models using multiple health data components.
method Multiview learning approach with Canonical Correlation Analysis (CCA) to generate features from different data views.
result Multiview representations enhance disease classification performance.
New insights into nonlinear multiview analysis for better data interpretation.
problem Identify shared latent components across different data views.
method Post-nonlinear model and multiview mixture learning.
result Identifies shared latent components under certain conditions.
With the rapid development of social media sharing, people often need to manage the growing volume of multimedia data such as large scale video classification and annotation, especially to organize those videos containing human activities. Recently, manifold regularized semi-supervised learning (SSL), which explores th…
A method for combining classifiers from multiple views using Bregman divergences.
problem Combining classifiers from multiple views with limited labeled data.
method Jointly learns view-specific and overall weighted majority vote classifiers using Bregman divergences.
result Empirical results show improved classifier performance with limited labeled data.
Proposes a co-hub node model for multiview graph learning.
problem Identifying shared graphical structures in heterogeneous datasets.
method Enforces structured sparsity on co-hub nodes across multiple views.
result Demonstrates improved precision and interpretive insight in learning multiview graphs.
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.
iDeepViewLearn combines deep learning and feature selection for multiview learning.
problem Learning nonlinear relationships in data from multiple complementary views.
method Combines deep learning flexibility with statistical feature selection using deep neural networks and graph Laplacian regularization.
result Identifies genes and CpG sites that differentiate between breast cancer survivors and non-survivors.
Proposes joint LCA for multiview data to identify shared and view-specific components.
problem Extracting shared components sequentially from multiview data.
method Formulates a matrix decomposition model with joint and individual structures, proposes a penalty term objective function, and employs a refitting procedure.
result Achieves simultaneous estimation and rank selection for cross covariance.
A novel GAN approach for multiview learning with missing data.
problem Learning from multiview data with missing observations.
method Biconditional GAN with two generators and a discriminator for conditional generation of missing views.
result The discriminator achieves significant classification performance and generators learn missing views without external resources.
Improved 3D scene understanding from partial point sets using multiview fusion.
problem Challenging task of 3D scene semantic understanding from partial point clouds.
method Multiview representation of 360° point clouds and fusion with original data.
result Overall increase of 31.9% and 4.3% in segmentation accuracy for partial and complete scenes.
Scalable methods integrate multiview data for clinical outcomes.
problem Jointly associate and predict outcomes from multiple data sources.
method Randomized Fourier bases for nonlinear mappings, view-independent low-dimensional representations.
result Identified molecular signatures for COVID-19 status and severity.
A new method for image annotation using multiview Hessian regularization.
problem Semi-supervised learning biases towards a constant function and is limited to single view data.
method Develops multiview Hessian regularization (mHR) to handle multiview data and improve generalization.
result mHR optimally combines multiple Hessian regularizations from different views to steer the classification function.
Proposes MvTPMSVM to improve multiview learning with reduced computational complexity.
problem Challenges in multiview learning, especially with heteroscedastic noise.
method Introduces MvTPMSVM, a parametric margin SVM model that avoids matrix inversions.
result Demonstrates superior generalization compared to baseline models.
This work explains how maximizing latent correlations across multiple data views helps in identifying shared and private components.
problem Understanding how to identify shared and private components in multiview data.
method An intuitive generative model of multiview data is adopted, and latent correlation maximization is shown to guarantee the extraction of shared components.
result Latent correlation maximization guarantees the extraction of shared components across views and disentangles private information.
This work improves density estimation by characterizing pdf complexity using NL-spectrum.
problem Improving density estimation rates for general probability densities.
method Introducing NL-spectrum to characterize pdf complexity and deriving dimension-independent rates of convergence.
result Dimension-independent rates of convergence for fast density estimation.
Bayesian models link multiview data to outcomes.
problem Inferring relationships between diverse data types and outcomes.
method Developed two factor regression models: JFR and JAFAR.
result Improved prediction of clinical outcomes from multi-omics data.
Develops a new method for solving generalized eigenvalue problems efficiently.
problem Efficiently solving generalized eigenvalue problems for large datasets.
method Inspired by the Generalized Hebbian Algorithm, develops a game-theory inspired approach to solving GEPs.
result Demonstrates state-of-the-art performance for optimizing Deep CCA.
Section 1.3 was incorrect, and 2.1 will be removed from further submissions. A rewritten version will be posted in the future.
A novel deep learning method predicts Twitter users' locations using multiple data types.
problem Predicting Twitter users' locations on large social networks.
method Combines content-based and network-based approaches using a multi-entry neural network architecture (MENET).
result MENET outperforms state-of-the-art methods by a large margin on three benchmark datasets.
Inserts proximal mapping into deep networks for better regularization.
problem Effective regularization of deep learning models to handle adversarial perturbations and correlations between modalities.
method Proposes a new layer that directly produces regularized hidden layer outputs using proximal mapping.
result Outperforms state-of-the-art methods in robust temporal learning and multiview modeling.
Unified CCA methods for large-scale data with fast SGD algorithms.
problem Computational infeasibility of classical CCA methods for large-scale data.
method Unconstrained objective, stochastic gradient descent (SGD) algorithms.
result Significantly faster convergence and higher correlations than previous methods.
A new method for combining multiple data views in supervised learning.
problem Combining multiple data views in supervised learning, especially in biology and medicine.
method Cooperative learning combines squared error loss with an agreement penalty to encourage predictions from different data views to agree.
result Cooperative learning achieves higher predictive accuracy on simulated and real multiomics data.
A new method clusters data from multiple sources using a mixture of multilayer SBMs.
problem Aggregating multiple clustering results from different data sources.
method Uses a mixture of multilayer Stochastic Block Models (SBM) to group co-membership matrices.
result Identifies and clusters observations based on their specificities within components.
Proposes GCCA for detecting latent relations in multiview data with sparse structures.
problem Sparse CCA limitations for multiple datasets.
method Developed a GCCA algorithm based on distributed alternating iteration approach.
result Demonstrated effectiveness on synthetic and real-world datasets.
DGCCA learns nonlinear transformations for multiple data views.
problem Learning informative nonlinear transformations from multiple data views.
method Deep Generalized Canonical Correlation Analysis (DGCCA) combines deep learning and generalized CCA.
result DGCCA representations outperform existing methods in phonetic transcription and hashtag recommendation.
ObSuRF converts a single image into a 3D model with NeRFs.
problem Creating a 3D model from a single image with object segmentation.
method Unsupervised volume segmentation using Neural Radiance Fields (NeRFs).
result ObSuRF can segment a 3D scene into objects from a single image.
New framework for large-scale SUMCOR GCCA with structural regularization.
problem Handling large-scale multiview data with structural regularization.
method Structured SUMCOR Multiview GCCA framework with scalable algorithms.
result Proposed algorithm converges to KKT point of regularized SUMCOR problem.
New method improves deep CCA by modeling private components conditionally independent of common factors.
problem Discovering latent co-variation in multiview datasets with weak common factors.
method Proposes a novel formulation that models private components conditionally independent of common factors.
result Validates the approach with synthetic and real datasets, showing improved identification of common factors.
A new ICA model identifies shared brain activity patterns across subjects.
problem Challenges in modeling shared responses in neuroimaging studies with large cohorts.
method MultiView Independent Component Analysis (ICA) model with closed-form likelihood and alternate quasi-Newton method.
result Improved sensitivity in identifying common brain activity patterns.
A new method for multi-criteria recommender systems using graph attention networks.
problem Lack of nuanced relationships between users and items based on specific criteria.
method MDGAT, a multi-edge bipartite graph with dual attention networks and contrastive learning.
result MDGAT achieves higher accuracy in predicting item ratings compared to baseline methods.
A new method combines multiple node embeddings using tensor decomposition.
problem Generating accurate node embeddings for complex networks.
method TenSemble2Vec: combines multiple embeddings via tensor decomposition.
result Improves node embeddings by leveraging complementary information from different methods.
In this paper, we address the challenging task of achieving multi-view dimensionality reduction. The goal is to effectively use the availability of multiple views for extracting a coherent low-dimensional representation of the data. The proposed method exploits the intrinsic relation within each view, as well as the mu…
This paper presents Correlated Nystrom Views (XNV), a fast semi-supervised algorithm for regression and classification. The algorithm draws on two main ideas. First, it generates two views consisting of computationally inexpensive random features. Second, XNV applies multiview regression using Canonical Correlation Ana…
Improved representations from multiple video views.
problem Learning reliable representations across unaligned video modalities.
method Correlation-based representation learning on a 4-way parallel multimodal dataset.
result Best models achieve retrieval rates up to 96.9% on open-domain instructional videos.
Novel algorithm estimates local permutations in unlabeled multi-view sensing.
problem Estimating local permutations in unlabeled multi-view sensing.
method Graph alignment and Gromov-Wasserstein alignment exploiting multiple views.
result The proposed algorithm is scalable and applicable to challenging SNR regimes.
Deep model integrates multityped representations for multimodal objects.
problem Modeling multityped, multimodal objects with precision and integration.
method Deep multitask architecture integrating multiple type-specific features.
result Model produces more compact and precise representation.
New framework tracks communities in dynamic networks.
problem Discovering and tracking communities in evolving networks.
method Spectral framework on Grassmann manifold for subspace tracking.
result Improved dynamic community detection results across various network types.
New methods for unsupervised learning of word and entity representations.
problem Learning distributed representations of words and entities from text and knowledge bases.
method MVLSA for words and NVSE for entities, both unsupervised learning methods.
result MVLSA and NVSE outperform state-of-the-art models in word and entity representation learning.
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.
The paper proposes a machine learning method to detect drivers' affective states using physiological signals.
problem Detecting and assessing drivers' affective states to improve driving safety and well-being.
method Multiview multi-task machine learning approach using physiological signals.
result Accounting for drive-specific differences significantly improves model performance.