A presentation for meeting of statistics in Japan.
https://connpass.com/event/204931/
Topics:
Methods and their properties to measure dependencies between variables
14. 参考・出典
14
Reshef, David N., et al. "Detecting novel associations in large data
sets." science 334.6062 (2011): 1518-1524.
MIC
HSIC
https://www.jst.go.jp/kisoken/aip/program/inter/vol2_sympo/slides/par
t1_2_yamada.pdf
Gretton, Arthur, et al. "Measuring statistical dependence with
Hilbert-Schmidt norms." International conference on algorithmic
learning theory. Springer, Berlin, Heidelberg, 2005.
https://www.ism.ac.jp/~fukumizu/OsakaU2014/OsakaU_6kernelMea
n.pdf
dCor
https://towardsdatascience.com/introducing-distance-correlation-a-su
perior-correlation-metric-d569dc8900c7
Székely, Gábor J., Maria L. Rizzo, and Nail K. Bakirov. "Measuring
and testing dependence by correlation of distances." The annals of
statistics 35.6 (2007): 2769-2794.
Friedman, Jerome, Trevor Hastie, and Robert Tibshirani. "Sparse
inverse covariance estimation with the graphical lasso." Biostatistics
9.3 (2008): 432-441.
Graphical Lasso
Witten, Daniela M., Jerome H. Friedman, and Noah Simon. "New
insights and faster computations for the graphical lasso." Journal of
Computational and Graphical Statistics 20.4 (2011): 892-900.
sGMRFmix
Idé, Tsuyoshi, Ankush Khandelwal, and Jayant Kalagnanam. "Sparse
Gaussian Markov random field mixtures for anomaly detection." 2016
IEEE 16th International Conference on Data Mining (ICDM). IEEE,
2016.
TVGL
Hallac, David, et al. "Network inference via the time-varying graphical
lasso." Proceedings of the 23rd ACM SIGKDD International
Conference on Knowledge Discovery and Data Mining. 2017.
15. 参考・出典
15
LSMI
CMI
PMI
HSICLasso
Jitkrittum, Wittawat, Hirotaka Hachiya, and Masashi Sugiyama. "Feature
Selection via< mos00099. jpg> 1-Penalized Squared-Loss Mutual
Information." IEICE TRANSACTIONS on Information and Systems 96.7
(2013): 1513-1524.
Novovičová, Jana, et al. "Conditional mutual information based feature
selection for classification task." Iberoamerican Congress on Pattern
Recognition. Springer, Berlin, Heidelberg, 2007.
Mukherjee, Sudipto, Himanshu Asnani, and Sreeram Kannan. "CCMI:
Classifier based conditional mutual information estimation." Uncertainty
in Artificial Intelligence. PMLR, 2020.
Zhao, Juan, et al. "Part mutual information for quantifying direct
associations in networks." Proceedings of the National Academy of
Sciences 113.18 (2016): 5130-5135.
Yamada, Makoto, et al. "High-dimensional feature selection by
feature-wise kernelized lasso." Neural computation 26.1 (2014):
185-207.
https://github.com/riken-aip/pyHSICLasso