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Kernel canonical correlation analysis (KCCA) is a nonlinear multi-view representation learning technique with broad applicability in statistics and machine learning.
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Kernel Methods in Computational Biology , chapter Heterogeneous Data Comparison and Gene Selection with Kernel Canonical Correlation Analysis, pp. 209–229
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Using KCCA for japanese-english cross-language information retrieval and classification
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Statistical consistency of kernel canonical correlation analysis
Fukumizu, Kenji, Bach, Francis R., and Gretton, Arthur · 2007
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Adaptive canonical correlation analysis based on matrix manifolds
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Deep canonical correlation analysis
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Stochastic optimization of PCA with capped MSG
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Lu, Yichao and Foster, Dean P · 2014
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Quasi-Monte Carlo feature maps for shift-invariant kernels
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Deep multilingual correlation for improved word embeddings
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