TreeCCA把XGBoost/LightGBM变成CCA编码器，效果不输深度学习，还自带特征重要性解释，适合表格数据多视图分析。
TreeCCA是首个用梯度提升树端到端训练典型相关分析编码器的方法，通过Eckart-Young损失函数在XGBoost、LightGBM中实现自定义目标。在Signed Power基准上，TreeCCA得分为2.61，超过Deep CCA的2.43；在Hermite上为2.93，接近2.89。在稀疏基准（p=50）上，TreeCCA的Precision@S达到1.00，而PMD无信号。UCI HAR传感器融合任务中，TreeCCA以5倍低成本达到与Deep CCA相当的精度，其增益重要性可直接验证物理假设。
TreeCCA: Canonical Correlation Analysis via Gradient-Boosted Trees
Gradient-boosted trees dominate tabular machine learning, yet canonical correlation analysis has always relied on linear or neural encoders. We propose \textbf{TreeCCA}, the first method to train gradient-boosted tree ensembles end-to-end as CCA encoders, inheriting their plug-and-play reliability: no architecture design, familiar hyperparameters, and strong performance with defaults. The technical enabler is the Eckart-Young (EY) loss, which supplies closed-form per-sample gradients that slot directly into any standard GBT library (XGBoost, LightGBM) as a custom objective. TreeCCA is the first CCA method to combine nonlinear accuracy with native interpretability: every tree split selects one feature, so gain importances reveal which inputs drive cross-view correlation at no extra cost. We demonstrate these properties on synthetic benchmarks, where TreeCCA matches or exceeds Deep CCA (2.61 vs.\ 2.43 on Signed Power; 2.93 vs.\ 2.89 on Hermite), and on a sparse benchmark with zero linear cross-view covariance, where TreeCCA recovers the true support with $\text{Precision@}S = 1.00$ at $p=50$ while PMD finds no signal. On the UCI HAR sensor-fusion benchmark, TreeCCA achieves comparable accuracy to Deep CCA at $5\times$ lower cost, while XGBoost gain importances directly validate a physics-motivated hypothesis about the data --- an interpretation not readily available with neural encoders. Across five popular tabular multi-view datasets, TreeMCCA consistently matches or exceeds linear CCA in both nonlinear correlation extraction and downstream classification accuracy.