Robust kernel CCA method detects outliers and improves performance.
problem Kernel CO and CCO sensitivity to contaminated data.
method Proposed robust kernel CO and CCO, derived IF for CCA, robust kernel CCA method.
result Robust kernel CCA method performs better than standard kernel CCA for ideal and contaminated data.
RKUM is an R package for robust kernel-based unsupervised methods.
problem Robust analysis under contaminated or noisy data conditions.
method Robust kernel covariance and cross-covariance operators using generalized loss functions.
result RKUM reduces sensitivity to contamination and effectively identifies outliers.
Robust methods for kernel CO and CCO improve unsupervised learning.
problem Sensitivity of kernel CO and CCO to contaminated data.
method Robust kernel CO and CCO based on generalized loss function, influence function, and visualization method.
result Robust kernel CCA shows superior performance over classical methods.
Robust method detects gene-gene interactions in imaging genetics data.
problem Detecting nonlinear gene-gene interactions in imaging genetics data.
method Robust Kernel Canonical Correlation Analysis (RKCCA) with influence function variance estimation.
result The proposed robust RKCCA method outperforms state-of-the-art methods in detecting gene-gene interactions.
Paper introduces RMEN-CCA for multi-view unsupervised learning.
problem Combining multiple data views for unsupervised learning.
method Robust matrix elastic net (RMEN) integrated with canonical correlation analysis (CCA).
result RMEN-CCA outperforms state-of-the-art methods on multiple datasets.
Paper proposes SKCCA for sparse kernel CCA, improving sparsity and reducing overfitting.
problem Lack of sparsity in kernel CCA solutions.
method Introduces SKCCA using ℓ1-regularization to penalize the dual vectors for sparsity. result Demonstrates improved sparsity and reduced overfitting in kernel CCA.
Kernel and MKCCA classify schizophrenia patients from imaging and genetic data.
problem Classifying schizophrenia patients from imaging and genetic data.
method Employed Kernel and Multiple Kernel Canonical Correlation Analysis (CCA) for classification.
result Kernel and Multiple Kernel CCA significantly outperform regularized linear CCA in classification accuracy.
Canonical correlation analysis (CCA) is a classical representation learning technique for finding correlated variables in multi-view data. Several nonlinear extensions of the original linear CCA have been proposed, including kernel and deep neural network methods. These approaches seek maximally correlated projections …
New method detects outliers in genetic and non-genetic data.
problem Identifying outliers in genetic and non-genetic data.
method Influence function of multiple kernel canonical correlation analysis.
result Visualization method effectively detects influential observations.
Canonical correlation analysis (CCA) is a valuable method for interpreting cross-covariance across related datasets of different dimensionality. There are many potential applications of CCA to neuroimaging data analysis. For instance, CCA can be used for finding functional similarities across fMRI datasets collected fr…
New similarity index avoids limitations of CCA in neural networks.
problem Limitations of existing methods in measuring neural network representation similarity.
method Introducing a similarity index based on centered kernel alignment (CKA) to measure representational similarity matrices.
result CKA reliably identifies correspondences between representations in networks trained from different initializations.
ORCCA improves CCA performance with randomized features.
problem Improving CCA performance with randomized features.
method Proposes a task-specific scoring rule for selecting random features in CCA.
result ORCCA outperforms Kernel CCA in expectation.
A novel graph-regularized CCA approach for datasets with a common source graph.
problem Discovering hidden sources in datasets with common geometry.
method Graph regularizer to encode common sources' geometry in CCA.
result Improved classification performance over competing methods.
A new method for identifying significant gene subsets improves disease prediction.
problem Identifying significant subsets of genes for disease prediction.
method Kernel gene shaving using influence function of kernel CCA.
result The proposed method outperformed three popular gene selection methods.
Canonical Correlation Analysis (CCA) is a classical tool for finding correlations among the components of two random vectors. In recent years, CCA has been widely applied to the analysis of genomic data, where it is common for researchers to perform multiple assays on a single set of patient samples. Recent work has pr…
Quantum-inspired CCA improves correlation analysis for high-dimensional data.
problem High-dimensional data limits conventional CCA due to time complexity.
method Developed a quantum-inspired CCA (qiCCA) with logarithmic time complexity.
result qiCCA extracts more correlations than linear CCA and is comparable to deep and kernel CCA.
Bayesian method improves sparse CCA for multi-view data.
problem Integrative statistical analysis of multi-view high-dimensional data.
method Bayesian infinite factor model with graphical horseshoe prior or diagonal structure to encourage sparsity.
result The proposed Bayesian ScSCCA approach achieves robust estimation of sparse CCA.
Kernel methods detect coherent structures in dynamical data.
problem Detecting coherent structures in complex dynamical systems.
method Kernel-based dimensionality reduction techniques and eigendecompositions of RKHS operators.
result Coherent sets of particle trajectories can be computed by kernel CCA.
We present a novel method for solving Canonical Correlation Analysis (CCA) in a sparse convex framework using a least squares approach. The presented method focuses on the scenario when one is interested in (or limited to) a primal representation for the first view while having a dual representation for the second view…
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.
Canonical Correlation Analysis (CCA) is a widely used statistical tool with both well established theory and favorable performance for a wide range of machine learning problems. However, computing CCA for huge datasets can be very slow since it involves implementing QR decomposition or singular value decomposition of h…
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.
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.
Proposes ℓ0-CCA for sparse CCA with improved representation learning.
problem CCA models break with too many variables, and sparsity is beneficial.
method Sparse CCA with stochastic gates and ℓ0-regularization. result Improves representation learning by gating nuisance variables.
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.
Paper discovers hidden common variables in nonlinear data.
problem Discover hidden common variables in nonlinear high-dimensional observations.
method Local CCA metric integrated with manifold learning.
result Metric discovers hidden common variables without rigid model assumptions.
End-to-end CCA optimizes both discriminative and latent space projections for multi-view learning.
problem Lack of class label information in CCA for multi-view learning tasks.
method Simultaneously optimizes a CCA-based and a task objective in an end-to-end manner to learn a non-linear CCA projection.
result Significant improvement in cross-view classification, regularization with a second view, and semi-supervised learning.
Unsupervised two-view learning, or detection of dependencies between two paired data sets, is typically done by some variant of canonical correlation analysis (CCA). CCA searches for a linear projection for each view, such that the correlations between the projections are maximized. The solution is invariant to any lin…
Proposes a probabilistic CCA with implicit distributions for multi-view data.
problem Overcoming the deficiency of linear correlation in practical multi-view learning tasks.
method Probabilistic interpretation of CCA based on implicit distributions, using Conditional Mutual Information (CMI) and Adversarial CCA (ACCA).
result Achieves superior alignment of multi-view data with implicit distributions.
Paper tackles fairness in CCA by minimizing correlation disparity error.
problem Fairness issues in CCA.
method Framework to minimize correlation disparity error in CCA.
result Reduces correlation disparity error without sacrificing CCA accuracy.
New measures link neural representation geometry to decoding ability.
problem Understanding how neural representations relate to decoding ability.
method Showed that popular similarity measures can be interpreted from a decoding perspective.
result Proved that measures like CKA and CCA quantify alignment between optimal linear readouts.
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…
A simple algorithm finds sparse correlations between two sets of variables.
problem Sparse CCA under standardized and uncorrelated variables.
method Combinatorial algorithm operating on low rank data approximation.
result Global approximation guarantees with precise sparsity control.
In this paper we define Crossing Change Alternating Knots (CCA knots) and their generalization: k-CCA knots.
This paper provides an overview of CCA-based multi-view learning approaches.
problem Fusing data from different sources or subsets.
method Canonical correlation analysis (CCA) for mapping data onto a common space with maximum correlation.
result Overview of many representative CCA-based multi-view learning approaches.
CCA features from medical codes predict future surgeries.
problem Predicting future surgeries based on medical codes.
method Canonical correlation analysis applied to sequences of medical codes.
result CCA embeddings capture meaningful relationships among medical codes.
Paper proposes ASCCA for sparse CCA with trace Lasso regularization.
problem Sparse CCA in high-dimensional settings with correlated variables.
method Trace Lasso regularization, reformulated to Riemannian manifolds, inexact augmented Lagrangian method.
result Improved stability and interpretation of sparse CCA.
A new method LDHA improves fMRI data alignment for cognitive state validation.
problem Accurate functional alignment across different subjects for MVP analysis.
method LDHA incorporates LDA into CCA for supervised fMRI alignment.
result LDHA outperforms other HA methods in MVP analysis.
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.
Two new methods for analyzing repeated measures data using embeddings into Reproducing Kernel Hilbert Spaces.
problem Analyzing complex data structures with multiple features over time.
method Two generalizations of canonical correlation analysis for repeated measures data using embeddings into Reproducing Kernel Hilbert Spaces.
result Consistency rates for transformation and correlation estimators, relaxing common assumptions.
New algorithms improve CCA with stochastic approximation.
problem Efficiently compute canonical correlation analysis.
method Inexact MSG and MEG algorithms for CCA.
result Achieves ε-suboptimality in poly(1/ε) iterations.
CRCCA framework improves non-linear CCA with compressed representations.
problem Non-linear CCA for multi-view data with limited samples.
method Information-theoretic compressed representation framework (CRCCA) based on lattice quantization.
result The CRCCA framework provides theoretical bounds and optimality conditions, offering a flexible and computationally efficient solution.
Sparse Canonical Correlation Analysis (CCA) has received considerable attention in high-dimensional data analysis to study the relationship between two sets of random variables. However, there has been remarkably little theoretical statistical foundation on sparse CCA in high-dimensional settings despite active methodo…
A new method for real-time CCA on streaming data.
problem Finding correlated features in online data streams.
method Sliding Window Informative Canonical Correlation Analysis (SWICCA) using streaming PCA.
result SWICCA provides real-time CCA components in high dimensions with theoretical guarantees.
New method solves sparse PCA and CCA with guaranteed convergence.
problem Sparse PCA and CCA for large-scale data analysis.
method Alternating manifold proximal gradient method.
result Unified convergence analysis for the proposed method.
New framework to test neural network representation similarity measures.
problem Disagreements among dissimilarity measures in neural networks.
method Statistical testing framework to evaluate measures based on functional behavior.
result Current metrics have different weaknesses; a classical baseline performs surprisingly well.
Canonical correlation analysis (CCA) has been one of the most popular methods for frequency recognition in steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs). Despite its efficiency, a potential problem is that using pre-constructed sine-cosine waves as the required reference signals in…
Graph Canonical Correlation Analysis improves CCA for multiomics datasets.
problem Limited ability of conventional CCA methods to incorporate structured patterns in cross-correlation matrices.
method Graph Canonical Correlation Analysis (gCCA) calculates canonical correlations based on the graph structure of cross-correlation matrices.
result gCCA outperforms competing CCA methods in simulations and multiomics dataset analysis.