Metadata-Version: 2.1
Name: cca_zoo
Version: 1.10.7
Summary: Canonical Correlation Analysis Zoo: A collection of Regularized, Deep Learning based, Kernel, and Probabilistic methods in a scikit-learn style framework
Home-page: https://github.com/jameschapman19/cca_zoo
Author: jameschapman
Author-email: james.chapman.19@ucl.ac.uk
License: MIT
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        # CCA zoo
        
        `cca-zoo` is a collection of linear, kernel, and deep methods for canonical correlation analysis of multiview data. 
        Where possible I have followed the `scikit-learn`/`mvlearn` APIs and models therefore have `fit`/`transform`/`fit_transform` methods as standard.
        
        ## Installation
        
        Dependency of some implemented algorithms are heavy, such as `pytorch` and `numpyro`. 
        We provide several options to accomandate the user's needs.
        For full details of algoritms inclused, please refere to section [Implemented Methods](#implemented-methods)
        
        Standard installation: 
        
        ```
        pip install cca-zoo
        ```
        For deep learning elements use:
        ```
        pip install cca-zoo[deep]
        ```
        
        For probabilistic elements use:
        ```
        pip install cca-zoo[probabilistic]
        ```
        ## Documentation
        Available at https://cca-zoo.readthedocs.io/en/latest/
          
        ## Citation:
        If this repository was helpful to you please do give a star.
        
        In case this work is used as part of research I attach a DOI bibtex entry:
        
        ```bibtex
        @software{james_chapman_2021_4925892,
          author       = {James Chapman and
                          Hao-Ting Wang},
          title        = {jameschapman19/cca\_zoo:},
          month        = jun,
          year         = 2021,
          publisher    = {Zenodo},
          doi          = {10.5281/zenodo.4382739},
          url          = {https://doi.org/10.5281/zenodo.4382739}
        }
        ```
        
        ## Implemented Methods
        
        ### Standard Install
        - CCA (Canonical Correlation Analysis): Solutions based on either alternating least squares or as the solution to genrralized eigenvalue problem
        - PLS (Partial Least Squares)
        - [rCCA (Ridge Regularized Canonical Correlation Analysis)](https://www.sciencedirect.com/science/article/abs/pii/0304407676900105?via%3Dihub)
        - [GCCA (Generalized CCA)](https://academic.oup.com/biomet/article-abstract/58/3/433/233349?redirectedFrom=fulltext)
        - MCCA (Multiset CCA)
        - K(M)CCA (kernel Multiset CCA)
        - [TCCA (Tensor CCA)](https://arxiv.org/pdf/1502.02330.pdf)
        - [KTCCA (kernel Tensor CCA)](https://arxiv.org/pdf/1502.02330.pdf)
        - [SCCA (Sparse CCA)](https://onlinelibrary.wiley.com/doi/abs/10.1111/biom.13043)
        - [SPLS (Sparse PLS/Penalized Matrix Decomposition](https://web.stanford.edu/~hastie/Papers/PMD_Witten.pdf)
        - [ElasticCCA (Penalized CCA)](https://pubmed.ncbi.nlm.nih.gov/19689958/)
        - [SWCCA (Sparse Weighted CCA)](https://arxiv.org/abs/1710.04792v1#:~:text=However%2C%20classical%20and%20sparse%20CCA%20models%20consider%20the,where%20weights%20are%20used%20for%20regularizing%20different%20samples)
        - [SpanCCA](http://akyrillidis.github.io/pubs/Conferences/cca.pdf)
        
        ### `[deep]` Install
        - DCCA (Deep CCA)
        
          Using either Andrew's original [Tracenorm Objective](https://ttic.uchicago.edu/~klivescu/papers/andrew_icml2013.pdf) or Wang's [alternating least squares solution](https://arxiv.org/pdf/1510.02054v1.pdf)
          
        - [DGCCA (Deep Generalized CCA)](https://www.aclweb.org/anthology/W19-4301.pdf)
        
          An alternative objective based on the linear GCCA solution. Can be extended to more than 2 views
         
        - [DMCCA (Deep Multiset CCA)](https://arxiv.org/abs/1904.01775)
        
          An alternative objective based on the linear MCCA solution. Can be extended to more than 2 views
          
        - [DTCCA (Deep Tensor CCA)](https://arxiv.org/pdf/2005.11914.pdf)
        - [DCCAE (Deep Canonically Correlated Autoencoders)](http://proceedings.mlr.press/v37/wangb15.pdf)
        - [DVCCA/DVCCA Private (Deep variational CCA)](https://arxiv.org/pdf/1610.03454.pdf)
        
        ### `[probabilistic]` Install
        - [Variational Bayes CCA](https://ieeexplore.ieee.org/document/4182407)
        
        ## Contributions
        A guide to contributions is available at https://cca-zoo.readthedocs.io/en/latest/developer_info/contribute.html
        
        ## Sources
        
        I've added this section to give due credit to the repositories that helped me in addition to their copyright notices in
        the code where relevant.
        
        ### Other Implementations of (regularised)CCA/PLS
        
        [MATLAB implementation](https://github.com/anaston/PLS_CCA_framework)
        
        ### Implementation of Sparse PLS
        
        MATLAB implementation of SPLS by [@jmmonteiro](https://github.com/jmmonteiro/spls)
        
        ### Other Implementations of DCCA/DCCAE
        
        Keras implementation of DCCA from [@VahidooX's github page](https://github.com/VahidooX)
        
        The following are the other implementations of DCCA in MATLAB and C++. These codes are written by the authors of the original paper:
        
        [Torch implementation](https://github.com/Michaelvll/DeepCCA) of DCCA from @MichaelVll & @Arminarj
        
        C++ implementation of DCCA from Galen Andrew's [website](https://homes.cs.washington.edu/~galen/)
        
        MATLAB implementation of DCCA/DCCAE from Weiran Wang's [website](http://ttic.uchicago.edu/~wwang5/dccae.html)
        
        MATLAB implementation of [TCCA](https://github.com/rciszek/mdr_tcca)
        
        ### Implementation of VAE
        
        [Torch implementation of VAE](https://github.com/pytorch/examples/tree/master/vae)
        
Keywords: cca
Platform: UNKNOWN
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Provides-Extra: deep
Provides-Extra: probabilistic
Provides-Extra: all
