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| ==Low Rank Plus Sparse Inverse == | | ==Low Rank Plus Sparse Inverse == |
| ==Experiments == | | ==Experiments == |
| ==Discussion==
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| RCA is an algorithm for describ- ing a low-dimensional representation of the residuals of a data set, given partial explanation by a covariance matrix <math>\Sigma</math>.The low-rank component of the model can be determined through a generalized eigenvalue problem. The paper illustrated how a treatment and a control time series could have their differences highlighted through appropriate selection of <math>\Sigma</math>(in this case we used an RBF kernel). The paper also introduced an algorithm for fitting a variant of CCA where the private spaces are explained through low dimensional latent variables.
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| Full covariance matrix model is often run into problem as their parameterization scales with <math>D^2</math>. This technique combined sparse-inverse covariance (as in GLASSO) with low rank (as in probabilistic PCA) approaches, and have good effect in the experiment.
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Revision as of 23:36, 4 July 2013
Maximum likelihood RCA
Low Rank Plus Sparse Inverse
Experiments