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        "author": "Xuefei Cao, Jun Ke, Xi Luo, Bjorn Sandstede",
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        "description": "# Time-dependent Canonical Correlation Analysis \nCanonical Correlation Analysis is a technique in multivariate data analysis for finding correlation and pairs of vectors that maximizes the correlation between a set of paired variables. Many important problems involve recoding time-dependent observations. In order to understand the coupling dynamics between the two sources, spot trends, detect anomalies, in this paper, we introduce the time-dependent canonical correlation analysis (TDCCA), a method of inferring time-dependent canonical vectors of paired variables.  \n\n\n## Getting Started\nWe provide both simulation examples used in our paper. The main computation algorithm is not added into the class method for the convenience of algorithm development for multi-view data. The package saves all the analysis to the given folder and saves the preprocessed data into the hdf5 file. The parallel computing with multi-cores is also allowed and tested in Linux system. This package also provides other algorithms to optimize the function, including cvxpy naive optimization, cvxpy naive admm optimization and two other admm algorithms. For details, see the admm_computation.py file. However, these functions have not been tested thoroughly. \n\nThese instructions will get you a copy of the project up running on your local machine for development and testing purposes. \n\nThis package is also published in pypi. For a quick installation, try\n\n```\npip install tdcca \n```\n\n### Prerequisites\n\nWhat things you need to install the software and how to install them\n\n```\nSee setup.py for details of packages requirements. \n```\n\n### Installing from GitHub\n\n\nDownload the packages by using git clone https://github.com/xuefeicao/tdcca.git\n\n```\npython setup.py install\n```\n\nIf you experience problems related to installing the dependency Matplotlib on OSX, please see https://matplotlib.org/faq/osx_framework.html \n\n### Intro to our package\nAfter installing our package locally, try to import tdcca in your python environment and learn about package's function. \nNote: our package name in pypi is tdcca. It is recommended that users scale the data before running our package. \n```\nfrom tdcca import *\nhelp(multi_sim)\n```\n\n\n### Examples\n```\nThe examples subfolder includes simulations provided in the paper. \n```\n\n## Running the tests\n\nThe test has been conducted in both Linux and Mac Os. \n\n## Built With\n\n* Python 2.7\n\n## Compatibility\n* Python 2.7\n\n\n## Authors\n\n* **Xuefei Cao** - *Maintainer* - (https://github.com/xuefeicao)\n* **Jun Ke**\n* **Xi Luo** (http://bigcomplexdata.com/)\n* **Bj\u00f6rn Sandstede** (http://www.dam.brown.edu/people/sandsted/)\n\n\n## License\n\nThis project is licensed under the MIT License - see the LICENSE file for details\n\n\n\n",
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