• Corpus ID: 202595202

Title Canonical dependency analysis based on squared-loss mutualinformation

@inproceedings{Karasuyama2019TitleCD,
  title={Title Canonical dependency analysis based on squared-loss mutualinformation},
  author={Masayuki Karasuyama and Masashi Sugiyama},
  year={2019}
}
Canonical correlation analysis (CCA) is a classical dimensionality reduction technique for two sets of variables that iteratively finds projection directions with maximum correlation. Although CCA is still in vital use in many practical application areas, recent real-world data often contain more complicated non-linear correlations that can not be properly captured by classical CCA. In this paper, we thus propose an extension of CCA that can effectively capture such complicated non-linear… 

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