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.
We study a two-level multiview learning with more than two views under the PAC-Bayesian framework. This approach, sometimes referred as late fusion, consists in learning sequentially multiple view-specific classifiers at the first level, and then combining these view-specific classifiers at the second level. Our main t…
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.
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.
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.
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.
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.
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.
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.
Multiview analysis aims at extracting shared latent components from data samples that are acquired in different domains, e.g., image, text, and audio. Classic multiview analysis, e.g., canonical correlation analysis (CCA), tackles this problem via matching the linearly transformed views in a certain latent domain. More…
Disease prediction or classification using health datasets involve using well-known predictors associated with the disease as features for the models. This study considers multiple data components of an individual's health, using the relationship between variables to generate features that may improve the performance o…
Multiview representation learning is very popular for latent factor analysis. It naturally arises in many data analysis, machine learning, and information retrieval applications to model dependent structures among multiple data sources. For computational convenience, existing approaches usually formulate the multiview …
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.
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.
We tackle the issue of classifier combinations when observations have multiple views. Our method jointly learns view-specific weighted majority vote classifiers (i.e. for each view) over a set of base voters, and a second weighted majority vote classifier over the set of these view-specific weighted majority vote class…
Canonical correlation analysis (CCA) is a popular technique for learning representations that are maximally correlated across multiple views in data. In this paper, we extend the CCA based framework for learning a multiview mixture model. We show that the proposed model and a set of simple heuristics yield improvements…
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…
In this paper we propose a boosting based multiview learning algorithm, referred to as PB-MVBoost, which iteratively learns i) weights over view-specific voters capturing view-specific information; and ii) weights over views by optimizing a PAC-Bayes multiview C-Bound that takes into account the accuracy of view-specif…
Deep learning within the context of point clouds has gained much research interest in recent years mostly due to the promising results that have been achieved on a number of challenging benchmarks, such as 3D shape recognition and scene semantic segmentation. In many realistic settings however, snapshots of the environ…
The rapid development of computer hardware and Internet technology makes large scale data dependent models computationally tractable, and opens a bright avenue for annotating images through innovative machine learning algorithms. Semi-supervised learning (SSL) has consequently received intensive attention in recent yea…
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.
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.
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.
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.
In this paper, we present a conditional GAN with two generators and a common discriminator for multiview learning problems where observations have two views, but one of them may be missing for some of the training samples. This is for example the case for multilingual collections where documents are not available in al…
Proposes Fusion Recurrent Neural Network for sequence data.
problem Improving sequence learning for practical applications.
method Fusion module and Transport module for sequence data.
result Fusion RNN performs comparably to state-of-the-art RNNs.
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.
Meta Fusion integrates various multimodal data fusion strategies into a unified framework.
problem Improving predictive power of machine learning methods across diverse applications.
method Meta Fusion constructs a cohort of models based on latent representations across modalities, sharing soft information to boost performance.
result Meta Fusion consistently outperforms conventional fusion strategies in simulation and real-world applications.
Section 1.3 was incorrect, and 2.1 will be removed from further submissions. A rewritten version will be posted in the future.
A new memory-based fusion layer improves multi-modal deep learning performance.
problem Improving performance of multi-modal deep learning by addressing long-term dependencies.
method Introducing a Memory based Attentive Fusion (MBAF) layer that incorporates both current and long-term dependencies.
result The MBAF layer enhances fusion and improves performance across different modalities and networks.
The sum-of-correlations (SUMCOR) formulation of generalized canonical correlation analysis (GCCA) seeks highly correlated low-dimensional representations of different views via maximizing pairwise latent similarity of the views. SUMCOR is considered arguably the most natural extension of classical two-view CCA to the m…
The paper analyzes how shared priors affect Bayesian data fusion performance.
problem Effect of shared priors on Bayesian data fusion performance.
method Theoretical analysis using two divergences common in Bayesian inference.
result Theoretical analysis and experimental validation of performance behavior.
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.
Adaptive data fusion boosts efficiency in multi-task optimization.
problem Multi-task non-smooth optimization in various fields.
method Adaptive data fusion approach leveraging commonalities among objectives.
result Significant improvements in sample efficiency with sharp statistical guarantees.
LMGPs enable efficient, accurate data fusion across multiple data sources.
problem Data fusion across multi-fidelity data sources in engineering design.
method Latent-map Gaussian processes (LMGPs) for efficient and accurate data fusion.
result LMGPs provide increased accuracy, reduced costs, and flexibility to fuse any number of data sources.
For most problems in science and engineering we can obtain data sets that describe the observed system from various perspectives and record the behavior of its individual components. Heterogeneous data sets can be collectively mined by data fusion. Fusion can focus on a specific target relation and exploit directly ass…
EmbraceNet fusion model for multi-sensor activity recognition.
problem Human activity recognition using multiple sensors is challenging.
method Independent processing of each sensor, feature fusion with EmbraceNet, post-processing, and additional processes.
result Improved performance in SHL recognition challenge.
Proposes clustering and pruning to simplify causal data fusion models.
problem Combining observational and experimental data to identify causal effects.
method Generalizes pruning and clustering operations for multiple data sources.
result Derives conditions for inferring causal effects from simplified models.
This paper proposes a novel framework for fusing multi-temporal, multispectral satellite images and OpenStreetMap (OSM) data for the classification of local climate zones (LCZs). Feature stacking is the most commonly-used method of data fusion but does not consider the heterogeneity of multimodal optical images and OSM…
New 4-manifold invariant defined from trisection diagrams.
problem Defining a new 4-manifold invariant from trisection diagrams.
method Algebraic data from bimodule categories and spherical fusion categories, described diagrammatically.
result Includes Hopf algebraic invariants and modular fusion category invariants.
Paper introduces topological eigenvalue theorems for tensor analysis in multi-modal data.
problem Lack of deep understanding of tensor structures in multi-modal data fusion.
method Introduces topological perspective to tensor eigenvalue analysis, linking eigenvalues to topological features.
result Establishes new theorems that enhance understanding of tensor structures in data fusion.
LDF combines neural networks with probabilistic models for data fusion.
problem Combining limited primary data with readily available auxiliary data.
method Neural networks as conjugate mappings of auxiliary data for posterior analysis.
result Efficient inference and compact latent variable posterior distributions.
Algorithms that fuse multiple input sources benefit from both complementary and shared information. Shared information may provide robustness against faulty or noisy inputs, which is indispensable for safety-critical applications like self-driving cars. We investigate learning fusion algorithms that are robust against …
The problem of predicting the location of users on large social networks like Twitter has emerged from real-life applications such as social unrest detection and online marketing. Twitter user geolocation is a difficult and active research topic with a vast literature. Most of the proposed methods follow either a conte…
Paper analyzes deep learning models for credit rating prediction using text and numerical data.
problem Improving credit rating prediction using multi-modal deep learning.
method Testing different deep learning models and fusion strategies for structured and unstructured datasets.
result CNN-based multi-modal model with two fusion strategies outperformed other models.
Paper proposes a new method for Bayesian linear regression using spike-and-slab priors.
problem Identifying predictors with similar relationships in linear regression models.
method Hierarchical Bayesian models with spike-and-slab priors and a Gibbs sampler.
result The proposed method outperforms previous methods in simulations and real data analysis.