Description Usage Arguments Details Value Note Author(s) References See Also Examples

Given two multi-dimensional data sets, find a pair of canonical projection pairs that maximizes the HSIC criterion. Called by hsicCCA, and intended for internal use, but users may play with it for potential finer controls.

1 | ```
hsicCCAfunc(x, y, Wx = NULL, Wy = NULL, sigmax, sigmay, numiter = 20, reltolstop = 1e-04)
``` |

`x` |
The x-variable data set. One row per observation. |

`y` |
The y-variable data set. One row per observation. |

`Wx` |
Initial projection vector for the x data set. Randomly set if NULL. |

`Wy` |
Initial projection vector for the y data set. Randomly set if NULL. |

`sigmax` |
The bandwidth parameter for the Gaussian kernel on the x-variable set. A positive value. The smaller the smoother. |

`sigmay` |
The bandwidth parameter for the Gaussian kernel on the y-variable set. A positive value. The smaller the smoother. |

`numiter` |
Maximum number of iterations. |

`reltolstop` |
Convergence threshold. Algorithm stops when relative changes in cost from consecutive iterations is less than the threshold. |

Optimization is done by gradient descent, where Nelder-Mead is used for step-size selection. Nelder Mead may fail to increase the cost at times (when stuck at local minima). User may consider restarting the algorithm when this happens.

A list containing:

`Wx ` |
The canoncial projection vector for the x-variable set. |

`Wy ` |
The canoncial projection vector for the y-variable set. |

`cost ` |
A vector of (negative) cost values at each iteration. |

Current implementation is slow and requires high storage for large sample data. Sample size > 2000 not recommended.

Billy Chang

Chang et. al. (2013) Canonical Correlation Analysis based on Hilbert-Schmidt Independence Criterion and Centered Kernel Target Alignment. ICML 2013.

Gretton et. al. (2005) Measuring statistical dependence with Hilbert-Schmidt Norm. In Algorithmic Learning Theory 2005.

1 2 3 4 5 6 7 8 9 10 11 12 | ```
set.seed(1)
numData <- 100
numDim <- 2
x <- matrix(rnorm(numData*numDim),numData,numDim)
y <- matrix(rnorm(numData*numDim),numData,numDim)
z <- runif(numData,-pi,pi)
y[,1] <- cos(z)+rnorm(numData,sd=0.1); x[,1] <- sin(z)+rnorm(numData,sd=0.1)
x <- scale(x)
y <- scale(y)
fit <- hsicCCAfunc(x,y,sigmax=1,sigmay=1)
plot(x%*%fit$Wx,y%*%fit$Wy)
``` |

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