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.
Paper proposes Adversarial CCA for multi-view alignment and generation.
problem Aligning multiple views in cross-view data analysis problems.
method Bayesian perspective, adversarial training for consistent latent encodings.
result ACCA model achieves superior performance in multi-view alignment and generation.
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.
New methods integrate nonlinear, sparse, and multi-view aspects for high-dimensional data analysis.
problem Integrating nonlinear dependence, sparsity, and multi-view data in high-dimensional datasets.
method Proposes HSIC-SGCCA, SA-KGCCA, and TS-KGCCA methods for multi-view high-dimensional data analysis.
result HSIC-SGCCA outperforms competing methods in multi-view variable selection.
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.
DICCA maps multi-view data into a shared latent space with interpretable components.
problem Learning from multiple related but distinct data views.
method DICCA extends CCA to deep generative networks and uses sparsity-inducing priors for interpretability.
result DICCA effectively disentangles shared and view-specific variations in multi-view data.
End-to-end deep learning for multi-view clustering improves accuracy across various data types.
problem Limited multi-view clustering methods for general data types and suboptimal two-stage process.
method Permutation-based canonical correlation objective for fused representations; pseudo-labels for clustering; theoretical error bound.
result Proposed model provides meaningful fused representations and effective clustering across multiple views.
Incremental clustering approaches have been proposed for handling large data when given data set is too large to be stored. The key idea of these approaches is to find representatives to represent each cluster in each data chunk and final data analysis is carried out based on those identified representatives from all t…
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.
ANN clusters multi-view data by agglomerating subviews and avoiding postprocessing.
problem Challenges in multi-view clustering, especially subview agglomeration.
method Agglomerative Neural Network (ANN) using Constrained Laplacian Rank.
result ANN outperforms state-of-the-art methods in multi-view clustering.
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.
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.
In this paper, we propose a speed-up approach for subclass discriminant analysis and formulate a novel efficient multi-view solution to it. The speed-up approach is developed based on graph embedding and spectral regression approaches that involve eigendecomposition of the corresponding Laplacian matrix and regression …
Multi-view data are increasingly prevalent in practice. It is often relevant to analyze the relationships between pairs of views by multi-view component analysis techniques such as Canonical Correlation Analysis (CCA). However, data may easily exhibit nonlinear relations, which CCA cannot reveal. We aim to investigate …
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.
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.
We propose a nonparametric Bayesian probabilistic latent variable model for multi-view anomaly detection, which is the task of finding instances that have inconsistent views. With the proposed model, all views of a non-anomalous instance are assumed to be generated from a single latent vector. On the other hand, an ano…
Proposes a multi-view VAE for imputing missing data from correlated sources.
problem Imputing missing data from multi-view sources with latent space correlation.
method Enforces a joint prior with latent space correlation between VAEs trained on each view.
result More strongly correlated latent spaces are uncovered, enabling effective imputation.
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.
Deep learning method clusters multi-view data matrices.
problem Clustering heterogeneous relational data matrices.
method Deep collective matrix tri-factorization (DCMTF).
result Discover latent clusters across input matrices and their associations.
Network analysis of human brain connectivity is critically important for understanding brain function and disease states. Embedding a brain network as a whole graph instance into a meaningful low-dimensional representation can be used to investigate disease mechanisms and inform therapeutic interventions. Moreover, by …
Bayesian model merges multi-view latent models and kernel methods.
problem Handling high-dimensionality and non-linear issues in multi-view data.
method Combines probabilistic factor analysis with kernelized observations.
result Compact solutions for kernelized observations and feature selection.
Proposes ACCA for better alignment of multiple data perspectives.
problem Unclear alignment between multiple data perspectives.
method Iteratively solves alignment and multi-view embedding.
result Improved alignment and embedding of multiple data perspectives.
Multi-view data clustering refers to categorizing a data set by making good use of related information from multiple representations of the data. It becomes important nowadays because more and more data can be collected in a variety of ways, in different settings and from different sources, so each data set can be repr…
Paper introduces a new histogram estimator for nonparametric density estimation that improves performance.
problem Smoothness-based nonparametric density estimators are not optimal for all types of data.
method Incorporates a multi-view latent variable model into histogram-style estimators.
result A new histogram estimator converges faster to multi-view models in L1 error. 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.
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.
Canonical Correlation Analysis (CCA) is a classic technique for multi-view data analysis. To overcome the deficiency of linear correlation in practical multi-view learning tasks, various CCA variants were proposed to capture nonlinear dependency. However, it is non-trivial to have an in-principle understanding of these…
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.
D-GCCA improves multi-view data analysis by separating common and distinctive components.
problem Analyzing multi-view high-dimensional data with latent factors.
method Decomposes each view's data matrix into common and distinctive sources with orthogonality constraints.
result Consistent estimators with good performance and efficient computation.
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.
This paper presents eight PAC-Bayes bounds to analyze the generalization performance of multi-view classifiers. These bounds adopt data dependent Gaussian priors which emphasize classifiers with high view agreements. The center of the prior for the first two bounds is the origin, while the center of the prior for the t…
Multi-view data, that is matched sets of measurements on the same subjects, have become increasingly common with advances in multi-omics technology. Often, it is of interest to find associations between the views that are related to the intrinsic class memberships. Existing association methods cannot directly incorpora…
Graph-based multi-view model predicts trading volume movement from various sources.
problem Lack of comprehensive understanding of trading volume movement from different sources.
method Graph-based approach incorporating long-term, short-term, and sudden event information.
result Our method outperforms strong baselines by a large margin.
Extends information bottleneck to multi-view unsupervised learning.
problem Identifying superfluous information in unlabeled multi-view data.
method Multi-view information bottleneck model, leveraging data augmentation.
result State-of-the-art results on Sketchy and MIR-Flickr datasets.
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.
MuVI models multi-view data with structured sparsity, integrating domain knowledge.
problem Disentangling variation across multiple data views in complex systems.
method Multi-view latent variable model with structured sparsity using a modified horseshoe prior.
result MuVI outperforms state-of-the-art methods in structured sparsity modeling and integrates noisy domain expertise.
Correlated component analysis as proposed by Dmochowski et al. (2012) is a tool for investigating brain process similarity in the responses to multiple views of a given stimulus. Correlated components are identified under the assumption that the involved spatial networks are identical. Here we propose a hierarchical pr…
This paper analyzes self-supervised learning from a multi-view perspective.
problem Understanding and optimizing self-supervised learning from multi-view data.
method Information-theoretical framework to understand and design self-supervised learning objectives.
result Self-supervised representations can extract task-relevant information and discard task-irrelevant information.
DTCCA learns nonlinear transformations of multi-view data for high-order correlation.
problem Learning complex nonlinear transformations of multiple data views.
method Maximizes high-order canonical correlation by jointly learning transformations of each view using a reformulated tensor decomposition.
result DTCCA efficiently handles high-dimensional and large number of views, overcoming scalability issues.
Factor analysis aims to determine latent factors, or traits, which summarize a given data set. Inter-battery factor analysis extends this notion to multiple views of the data. In this paper we show how a nonlinear, nonparametric version of these models can be recovered through the Gaussian process latent variable model…
Study community detection in multi-view data with various types of information.
problem Community detection in multi-view data with different types of information.
method Unified theoretical framework, mutual information analysis, sharp thresholds, iterative algorithms.
result Sharp thresholds for community recovery in various multi-view settings.
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.
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…
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.
Multi-view learning (MVL) is a strategy for fusing data from different sources or subsets. Canonical correlation analysis (CCA) is very important in MVL, whose main idea is to map data from different views onto a common space with maximum correlation. Traditional CCA can only be used to calculate the linear correlation…
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.
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.