Information Retrieval
Kernel independent component analysis
The Journal of Machine Learning Research
Using KCCA for Japanese---English cross-language information retrieval and document classification
Journal of Intelligent Information Systems
Kernel Methods for Measuring Independence
The Journal of Machine Learning Research
Accurate Error Bounds for the Eigenvalues of the Kernel Matrix
The Journal of Machine Learning Research
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
The Journal of Machine Learning Research
Statistical Consistency of Kernel Canonical Correlation Analysis
The Journal of Machine Learning Research
Discriminating image senses by clustering with multimodal features
COLING-ACL '06 Proceedings of the COLING/ACL on Main conference poster sessions
SURF: speeded up robust features
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Exploiting tag and word correlations for improved webpage clustering
SMUC '10 Proceedings of the 2nd international workshop on Search and mining user-generated contents
Semi-supervised kernel canonical correlation analysis with application to human fMRI
Pattern Recognition Letters
Dimensionality reduction by Mixed Kernel Canonical Correlation Analysis
Pattern Recognition
Leveraging Social Bookmarks from Partially Tagged Corpus for Improved Web Page Clustering
ACM Transactions on Intelligent Systems and Technology (TIST)
Neighborhood Correlation Analysis for Semi-paired Two-View Data
Neural Processing Letters
Multi-view classification with cross-view must-link and cannot-link side information
Knowledge-Based Systems
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Kernel canonical correlation analysis (KCCA) is a fundamental technique for dimensionality reduction for paired data. By finding directions that maximize correlation in the space implied by the kernel, KCCA is able to learn representations that are more closely tied to the underlying semantics of the data rather than high variance directions, which are found by PCA but may be the result of noise. However, meaningful directions are not only those that have high correlation to another modality, but also those that capture the manifold structure of the data. We propose a method that is able to simultaneously find highly correlated directions that are also located on high variance directions along the data manifold. This is achieved by the use of semi-supervised Laplacian regularization in the formulation of KCCA, which has the additional benefit of being able to use additional data for which correspondence between the modalities is not known to more robustly estimate the structure of the data manifold. We show experimentally on datasets of images and text that Laplacian regularized training improves the class separation over KCCA with only Tikhonov regularization, while causing no degradation in the correlation between modalities. We propose a model selection criterion based on the Hilbert-Schmidt norm of the semi-supervised Laplacian regularized cross-covariance operator, which can be computed in closed form. Kernel canonical correlation analysis (KCCA) is a dimensionality reduction technique for paired data. By finding directions that maximize correlation, KCCA learns representations that are more closely tied to the underlying semantics of the data rather than noise. However, meaningful directions are not only those that have high correlation to another modality, but also those that capture the manifold structure of the data. We propose a method that is simultaneously able to find highly correlated directions that are also located on high variance directions along the data manifold. This is achieved by the use of semi-supervised Laplacian regularization of KCCA. We show experimentally that Laplacian regularized training improves class separation over KCCA with only Tikhonov regularization, while causing no degradation in the correlation between modalities. We propose a model selection criterion based on the Hilbert-Schmidt norm of the semi-supervised Laplacian regularized cross-covariance operator, which we compute in closed form.