mvlearn: Multiview Machine Learning in Python']Python

被引:0
|
作者
Perry, Ronan [1 ]
Mischler, Gavin [10 ]
Guo, Richard [2 ]
Lee, Theodore [1 ]
Chang, Alexander [1 ]
Koul, Arman [1 ]
Franz, Cameron [2 ]
Richard, Hugo [5 ]
Carmichael, Iain [6 ]
Ablin, Pierre [7 ,8 ]
Gramfort, Alexandre [5 ]
Vogelstein, Joshua T. [1 ,3 ,4 ,9 ]
机构
[1] Johns Hopkins Univ, Dept Biomed Engn, Baltimore, MD 21218 USA
[2] Johns Hopkins Univ, Dept Comp Sci, Baltimore, MD 21218 USA
[3] Johns Hopkins Univ, Ctr Imaging Sci, Baltimore, MD 21218 USA
[4] Johns Hopkins Univ, Kavli Neurosci Discovery Inst, Inst Computat Med, Baltimore, MD 21218 USA
[5] Univ Paris Saclay, INRIA, Palaiseau, France
[6] Univ Washington, Dept Stat, Seattle, WA 98195 USA
[7] PSL Univ, Ecole Normale Super, CNRS, Paris, France
[8] PSL Univ, Ecole Normale Super, DMA, Paris, France
[9] Progress Learning, Baltimore, MD 21218 USA
[10] Columbia Univ, Dept Elect Engn, New York, NY 10027 USA
关键词
multiview; machine learning; !text type='python']python[!/text; multi-modal; multi-table; multi-block; CANONICAL CORRELATION-ANALYSIS;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
As data are generated more and more from multiple disparate sources, multiview data sets, where each sample has features in distinct views, have grown in recent years. However, no comprehensive package exists that enables non-specialists to use these methods easily. mvlearn is a Python library which implements the leading multiview machine learning methods. Its simple API closely follows that of scikit-learn for increased ease-of-use. The package can be installed from Python Package Index (PyPI) and the conda package manager and is released under the MIT open-source license. The documentation, detailed examples, and all releases are available at https://mvlearn.github.io/.
引用
收藏
页数:7
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