Canonical Correlation Analysis based on Hilbert-Schmidt Independence Criterion and Centered Kernel Target Alignment
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Summary
Two nonlinear CCA extensions that rely on the recently proposed Hilbert-Schmidt independence criterion and the centered kernel target alignment are introduced that determine linear projections that provide maximally dependent projected data pairs.
- Type
- article
- Published
- 2013-06-16
- Cited by
- 51
- References
- 25
- OpenAlex
- https://openalex.org/W205396393
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:660253
Keywords
Canonical correlation, Interpretability, Independence (probability theory), Robustness (evolution), Kernel (algebra)
References
- Nonlinear canonical correlation analysis by neural networks
- Large-scale kernel machines
- Kernel independent component analysis
- Algorithms for Learning Kernels Based on Centered Alignment
- Sparse Canonical Correlation Analysis with Application to Genomic Data Integration
- Ordinal Measures of Association
- Relations Between Two Sets of Variates
- Hedonic housing prices and the demand for clean air
- The Geometry of Algorithms with Orthogonality Constraints
- Kernel dimension reduction in regression
- Sparse canonical correlation analysis
- Canonical Correlation Analysis: An Overview with Application to Learning Methods
- The ILIUM forward modelling algorithm for multivariate parameter estimation and its application to derive stellar parameters from Gaia spectrophotometry
- Unsupervised Kernel Dimension Reduction
- Feature Selection via Dependence Maximization
- Fast Kernel-Based Independent Component Analysis
- Deflation based nonlinear canonical correlation analysis
- Hilbert Space Embeddings of Hidden Markov Models
- integrOmics: an R package to unravel relationships between two omics datasets
- Sparse canonical correlation analysis, with applications to genomic data
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- Learning Kernels for Structured Prediction using Polynomial Kernel Transformations
- Canonical Divergence Analysis
- Linear-time Detection of Non-linear Changes in Massively High Dimensional Time Series
- Robust Kernel (Cross-) Covariance Operators in Reproducing Kernel Hilbert Space toward Kernel Methods
- Learning Linear Representation of Space Partitioning Trees Based on Unsupervised Kernel Dimension Reduction
- Learning schizophrenia imaging genetics data via Multiple Kernel Canonical Correlation Analysis
- Kernel Machines are Not Black Boxes - On the Interpretability of Kernel-based Nonparametric Models
- Sparse kernel canonical correlation analysis for discovery of nonlinear interactions in high-dimensional data
- Informative Data Fusion: Beyond Canonical Correlation Analysis
- Multiple kernel learning with hybrid kernel alignment maximization
- Distance Covariance Analysis
- Kernel Method for Detecting Higher Order Interactions in multi-view Data: An Application to Imaging, Genetics, and Epigenetics
- Non-parametric Methods for Correlation Analysis in Multivariate Data with Applications in Data Mining
- A Tutorial on Canonical Correlation Methods
- Deep Matching Autoencoders
- A study of Bangladesh's sub-surface water storages using satellite products and data assimilation scheme.
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