mlsanityData to 14 Oct 2026

Generalized Canonical Correlation Analysis for Classification

arXiv 1304.7981 ↗First posted 30 Apr 2013stat.MLcited by 7

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Abstract (arXiv)

For multiple multivariate data sets, we derive conditions under which Generalized Canonical Correlation Analysis (GCCA) improves classification performance of the projected datasets, compared to standard Canonical Correlation Analysis (CCA) using only two data sets. We illustrate our theoretical results with simulations and a real data experiment.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). First posted on arXiv 30 Apr 2013.

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Citing papers per year: 2021: 3; 2022: 0; 2023: 1; 2024: 3.

7 papers.

  1. 2023Synergistic Graph Fusion via Encoder Embeddingcited by 6
  2. 2024Refined Graph Encoder Embedding via Self-Training and Latent Community Recoverycited by 5
  3. 2021Sparse GCA and Thresholded Gradient Descentcited by 4
  4. 2024Encoder Embedding for General Graph and Node Classificationcited by 4
  5. 2021The Labeled Multiple Canonical Correlation Analysis for Information Fusioncited by 2
  6. 2021Non-Euclidean Analysis of Joint Variations in Multi-Object Shapescited by 0
  7. 2024Synergistic eigenanalysis of covariance and Hessian matrices for enhanced binary classificationcited by 0