Canonical correlation analysis (CCA) is a method for finding statistical dependencies between two data sources, used for multi-view learning tasks (Hardoon et ...
This work proposes a new Bayesian CCA variant that is computationally efficient and works for high-dimensional data, while also learning the factorization ...
We propose a new Bayesian CCA variant that is computationally efficient and works for high-dimensional data, while also learning the factorization more ...
Sep 11, 2015 · We propose a new Bayesian CCA variant that is computationally efficient and works for high-dimensional data, while also learning the ...
Bayesian CCA via Group Sparsity · Helsinki Institute for Information Technology · Department of Computer Science. Research output: Contribution to journal ...
Bayesian CCA via Group Sparsity. Bayesian CCA via Group Sparsity. Year of publication. 2011. Authors. Virtanen, Seppo; Klami, Arto; Kaski, Samuel. Organizations ...
Search by expertise, name or affiliation. Bayesian CCA via Group Sparsity. Seppo Virtanen, Arto Klami, Samuel Kaski. Research output: Chapter in Book/Report ...
Here, we review current work in sparse latent factor models and describe our Bayesian group factor Analysis with Structured Sparsity (BASS) model in the context ...
We review recent developments in Bayesian models and inference methods for CCA which are attractive for their potential in hierarchical extensions.
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Apr 1, 2022 · GFA is a promising tool that can be used to uncover associations between and within multiple data modalities in benchmark datasets.