Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Kernel independent component analysis
The Journal of Machine Learning Research
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Multi-Output Regularized Feature Projection
IEEE Transactions on Knowledge and Data Engineering
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
The Journal of Machine Learning Research
Sparse eigen methods by D.C. programming
Proceedings of the 24th international conference on Machine learning
Least squares linear discriminant analysis
Proceedings of the 24th international conference on Machine learning
A least squares formulation for a class of generalized eigenvalue problems in machine learning
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Linear dimensionality reduction for multi-label classification
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
On the equivalence between canonical correlation analysis and orthonormalized partial least squares
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Super-resolution of human face image using canonical correlation analysis
Pattern Recognition
A shared-subspace learning framework for multi-label classification
ACM Transactions on Knowledge Discovery from Data (TKDD)
A scalable two-stage approach for a class of dimensionality reduction techniques
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Manifold elastic net: a unified framework for sparse dimension reduction
Data Mining and Knowledge Discovery
Multivariate regression shrinkage and selection by canonical correlation analysis
Computational Statistics & Data Analysis
Learning canonical correlations of paired tensor sets via tensor-to-vector projection
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Canonical Correlation Analysis (CCA) is a well-known technique for finding the correlations between two sets of multi-dimensional variables. It projects both sets of variables into a lower-dimensional space in which they are maximally correlated. CCA is commonly applied for supervised dimensionality reduction, in which one of the multi-dimensional variables is derived from the class label. It has been shown that CCA can be formulated as a least squares problem in the binaryclass case. However, their relationship in the more general setting remains unclear. In this paper, we show that, under a mild condition which tends to hold for high-dimensional data, CCA in multi-label classifications can be formulated as a least squares problem. Based on this equivalence relationship, we propose several CCA extensions including sparse CCA using 1-norm regularization. Experiments on multi-label data sets confirm the established equivalence relationship. Results also demonstrate the effectiveness of the proposed CCA extensions.