>* [Linear and Quadratic Discriminant Analysis with covariance ellipsoid](https://scikit-learn.org/stable/auto_examples/classification/plot_lda_qda.html): LDA和QDA在特定数据上的对比
> **示例:**
>
> * [Linear and Quadratic Discriminant Analysis with covariance ellipsoid](https://scikit-learn.org/stable/auto_examples/classification/plot_lda_qda.html):LDA和QDA在特定数据上的对比
`eigen`(特征) solver 是基于 class scatter (类散度)与 class scatter ratio (类内离散率)之间的优化。 它可以被用于 classification (分类)以及 transform (转换),此外它还同时支持收缩。然而,该解决方案需要计算协方差矩阵,因此它可能不适用于具有大量特征的情况。
>* [Normal and Shrinkage Linear Discriminant Analysis for classification](https://scikit-learn.org/stable/auto_examples/classification/plot_lda.html#sphx-glr-auto-examples-classification-plot-lda-py): Comparison of LDA classifiers with and without shrinkage.
>* [Normal and Shrinkage Linear Discriminant Analysis for classification](https://scikit-learn.org/stable/auto_examples/classification/plot_lda.html#sphx-glr-auto-examples-classification-plot-lda-py:有收缩和无收缩LDA分类器的比较。
> **参考资料**:
> * [3] “The Elements of Statistical Learning”, Hastie T., Tibshirani R., Friedman J., Section 4.3, p.106-119, 2008.
> * [4] Ledoit O, Wolf M. Honey, I Shrunk the Sample Covariance Matrix. The Journal of Portfolio Management 30(4), 110-119, 2004.
> **参考资料:**
>
> * **3([1](https://scikit-learn.org/stable/modules/lda_qda.html#id1),[2](https://scikit-learn.org/stable/modules/lda_qda.html#id2))** “The Elements of Statistical Learning”, Hastie T., Tibshirani R., Friedman J., Section 4.3, p.106-119, 2008.
> * **[[4]](https://scikit-learn.org/stable/modules/lda_qda.html#id3)** Ledoit O, Wolf M. Honey, I Shrunk the Sample Covariance Matrix. The Journal of Portfolio Management 30(4), 110-119, 2004.