Search Machine Learning Repository: @inproceedings{icml2014c2_mukuta14,
    Publisher = {JMLR Workshop and Conference Proceedings},
    Title = {Probabilistic Partial Canonical Correlation Analysis},
    Url = {http://jmlr.org/proceedings/papers/v32/mukuta14.pdf},
    Abstract = {Partial canonical correlation analysis (partial CCA) is a statistical method that estimates a pair of linear projections onto a low dimensional space, where the correlation between two multidimensional variables is maximized after eliminating the influence of a third variable. Partial CCA is known to be closely related to a causality measure between two time series. However, partial CCA requires the inverses of covariance matrices, so the calculation is not stable. This is particularly the case for high-dimensional data or small sample sizes. Additionally, we cannot estimate the optimal dimension of the subspace in the model. In this paper, we have addressed these problems by proposing a probabilistic interpretation of partial CCA and deriving a Bayesian estimation method based on the probabilistic model. Our numerical experiments demonstrated that our methods can stably estimate the model parameters, even in high dimensions or when there are a small number of samples.},
    Author = {Yusuke Mukuta and Tatsuya Harada},
    Editor = {Tony Jebara and Eric P. Xing},
    Year = {2014},
    Booktitle = {Proceedings of the 31st International Conference on Machine Learning (ICML-14)},
    Pages = {1449-1457}
   }