Robust canonical correlation and correspondence analysis
ICOSCO-I conference proceedings on The frontiers of statistical scientific theory & industrial applications (Vol. II)
Machine Learning
A neural implementation of canonical correlation analysis
Neural Networks
Nonlinear canonical correlation analysis by neural networks
Neural Networks
Combining Artificial Neural Nets: Ensemble and Modular Multi-Net Systems
Combining Artificial Neural Nets: Ensemble and Modular Multi-Net Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiclassifier Systems: Back to the Future
MCS '02 Proceedings of the Third International Workshop on Multiple Classifier Systems
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
Journal of Multivariate Analysis
Robust probabilistic projections
ICML '06 Proceedings of the 23rd international conference on Machine learning
Correlating multiple SNPs and multiple disease phenotypes
Bioinformatics
On voting-based consensus of cluster ensembles
Pattern Recognition
Variational Bayesian mixture of robust CCA models
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part III
Combining multiple clusterings using similarity graph
Pattern Recognition
Canonical correlation analysis using within-class coupling
Pattern Recognition Letters
Semi-supervised kernel canonical correlation analysis with application to human fMRI
Pattern Recognition Letters
Combining multiple clusterings using fast simulated annealing
Pattern Recognition Letters
Expert Systems with Applications: An International Journal
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
Engineering Applications of Artificial Intelligence
Parallel multi-swarm optimizer for gene selection in DNA microarrays
Applied Intelligence
Ensemble-based regression analysis of multimodal medical data for osteopenia diagnosis
Expert Systems with Applications: An International Journal
Dynamic clustering using combinatorial particle swarm optimization
Applied Intelligence
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Canonical Correlation Analysis (CCA) aims at identifying linear dependencies between two different but related multivariate views of the same underlying semantics. Ignoring its various extensions to more than two views, CCA uses these two views as complex labels to guide the search of maximally correlated projection vectors (covariates). Therefore, CCA can overfit the training data, meaning that different correlated projections can be found when the two-view training dataset is resampled. Although, to avoid such overfitting, ensemble approaches that utilize resampling techniques have been effectively used for improving generalization of many machine learning methods, an ensemble approach has not yet been formulated for CCA. In this paper, we propose an ensemble method for obtaining a final set of covariates by combining multiple sets of covariates extracted from subsamples. In comparison to those obtained by the application of the classical CCA on the whole set of training data, combining covariates with weaker correlations extracted from a number of subsamples of the training data produces stronger correlations that generalize to unseen test examples. Experimental results on emotion recognition, digit recognition, content-based retrieval, and multiple view object recognition have shown that ensemble CCA has better generalization for both the test set correlations of the covariates and the test set accuracy of classification performed on these covariates.