Kaczmarz-inspired acceleration for kernel canonical correlation analysis: Theory, algorithms, and its applications to information retrieval.
- DOI
- 10.1016/j.neunet.2026.109680
- Published
- 2027 Feb
- Container
- Neural networks : the official journal of the International Neural Network Society
- Publisher
- Not recorded
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1016/j.neunet.2026.109680,
title = {Kaczmarz-inspired acceleration for kernel canonical correlation analysis: Theory, algorithms, and its applications to information retrieval.},
author = {Zuo Q and Zhang L and Liao X and Xuan H},
year = {2027},
journal = {Neural networks : the official journal of the International Neural Network Society},
doi = {10.1016/j.neunet.2026.109680},
url = {https://doi.org/10.1016/j.neunet.2026.109680}
}RIS
TY - JOUR TI - Kaczmarz-inspired acceleration for kernel canonical correlation analysis: Theory, algorithms, and its applications to information retrieval. AU - Zuo Q AU - Zhang L AU - Liao X AU - Xuan H PY - 2027 JO - Neural networks : the official journal of the International Neural Network Society DO - 10.1016/j.neunet.2026.109680 UR - https://doi.org/10.1016/j.neunet.2026.109680 ER -
APA
Q, Z., L, Z., X, L., & H, X. (2027). Kaczmarz-inspired acceleration for kernel canonical correlation analysis: Theory, algorithms, and its applications to information retrieval.. Neural networks : the official journal of the International Neural Network Society. https://doi.org/10.1016/j.neunet.2026.109680
Source records
- pubmed · retrieved 2026-11-10T19:00:13.431Z