Nonlinear canonical correlation analysis by neural networks
Neural Networks
Kernel and Nonlinear Canonical Correlation Analysis
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 4 - Volume 4
Clustering Incomplete Data Using Kernel-Based Fuzzy C-means Algorithm
Neural Processing Letters
Nonlinear Signal Estimation using KernelWiener Filter in Canonical Correlation Analysis Framework
CIMCA '05 Proceedings of the International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce Vol-1 (CIMCA-IAWTIC'06) - Volume 01
Pattern Recognition, Third Edition
Pattern Recognition, Third Edition
Locality preserving CCA with applications to data visualization and pose estimation
Image and Vision Computing
Statistical Consistency of Kernel Canonical Correlation Analysis
The Journal of Machine Learning Research
Proceedings of the 24th international conference on Machine learning
An RKHS for multi-view learning and manifold co-regularization
Proceedings of the 25th international conference on Machine learning
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Subset based least squares subspace regression in RKHS
Neurocomputing
A new method of feature fusion and its application in image recognition
Pattern Recognition
Multi-view discriminative sequential learning
ECML'05 Proceedings of the 16th European conference on Machine Learning
Multiset canonical correlations analysis and multispectral, truly multitemporal remote sensing data
IEEE Transactions on Image Processing
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Canonical correlation analysis (CCA) is a well-known technique for extracting linearly correlated features from multiple views (i.e., sets of features) of data. Recently, a locality-preserving CCA, named LPCCA, has been developed to incorporate the neighborhood information into CCA. Although LPCCA is proved to be better in revealing the intrinsic data structure than CCA, its discriminative power for subsequent classification is low on high-dimensional data sets such as face databases. In this paper, we propose an alternative formulation for integrating the neighborhood information into CCA and derive a new locality-preserving CCA algorithm called ALPCCA, which can better discover the local manifold structure of data and further enhance the discriminative power for high-dimensional classification. The experimental results on both synthetic and real-world data sets including multiple feature data set and face databases validate the effectiveness of the proposed method.