Sparse CCA通过在CCA的基础上添加稀疏约束,实现了在保持最大相关性的同时,简化了模型并提高了可解释性。这对于处理高维数据集尤其有用,因为高维数据集中往往包含大量冗余或无关的变量。
Python implementations for Sparse CCA algorithms. Includes: Sparse (multiple) CCA based on Penalized Matrix Decomposition (PMD) from Witten et al, 2009. Sparse CCA based on Iterative Penalized Least Squares from Mai et al, 2019. One main difference between these two is that while the first is...
Sparse cca: Adaptive estimation and computational barriers. The Annals of Statistics, to appear .C. Gao, Z. Ma, and H. H. Zhou. Sparse CCA: Adaptive estimation and computational barriers. ArXiv e-prints, September 2014.Gao, C., Ma, Z., and Zhou, H. H. (2015). Sparse cca: ...
Sparse2DCCA是一种二维稀疏典型相关分析方法,主要用于盲源分离和脑图像分析。与传统的典型相关分析方法不同,Sparse2DCCA可以通过考虑数据中的空间结构信息来提高分离效果和分析结果的可解释性。具体地,Sparse2DCCA利用了两个数据集之间的潜在关联性,同时将空间信息建模为一个稀疏的二维结构。因此,该方法可以有效地减少...
zero[22,28,29]andthecorrespondingmodelisreferredtosparseCCA.However, thesimple 1 -normpenaltyislimitedinthatitneglectstherichstructuralinformation amongvariables. When dealing with high-dimensional data, prior structural knowl- edge is crucial for improving the estimation performance and model interpretabilit...
Sparse CCA (sCCA) methods were introduced to overcome such difficulty, mostly using penalizations with l-1 norm (CCA-l1) or the combination of l-1and l-2 norm (CCA-elastic net). However, they overlook the structural or group effect within genomic data in the analysis, which often exist ...
Sparse canonical correlation analysis作者:David R. Hardoon, John Shawe-Taylor 摘要 We present a novel method for solving Canonical Correlation Analysis (CCA) in a sparse convex framework using a least squares approach. The presented method focuses on the scenario when one is interested in (or lim...
We present a novel method for solving Canonical Correlation Analysis (CCA) in a sparse convex framework using a least squares approach. The presented metho
Monte Carlo simulations and real-data analysis are conducted to examine the efficiency of the proposed sparse CCA. We observe from the numerical studies that our strategies can incorporate sparsity into the common loading estimation and efficiently recover a sparse common structure efficiently in ...
(e.g., SNPs). Sparse CCA (sCCA) methods were introduced to overcome such difficulty, mostly using penalizations withl-1 norm (CCA-l1) or the combination ofl-1andl-2 norm (CCA-elastic net). However, they overlook the structural or group effect within genomic data in the analysis, which...