# Complementary Material

**Chapter 1**

The fMRI data set used in the Example 1.1 can be downloaded here. To be able to run Example 1.1 download ex11. You also need cca.m, sorteig.m, im_resize.m and T1.mat.
**Chapter 2**

The fMRI data set used in the Example 2.2 (the same as in Example 1.1) can be downloaded here. An implementation of lasso can be found here here. I have used lassoGaussSeidel.
To handle the fMRI data set, data was spatially smoothed and detrended voxel-by-voxel. In order to separate voxels measuring
brain activity from those that are not, a time series from a voxel was set to zero if the absolut sum
of the time series was lower than some threshold. Some code for this can be found here. The data set X_filter can be downloaded here.
**Chapter 3**

To run example 3.2 the following files are needed: ex32.m, fit.m, k.m, adjacency.m and L2_distance.m. adjacency.m and L2_distance.m can be downloaded here.
**Chapter 4**

Code for the manifold learning algorithm Locally Linear Embedding (LLE) can be dowloaded from the LLE page.
**Chapter 5**

To run Example 5.7 download ex57.m
after having downloaded WDMR.m, nn.m,
fit.m, mapconv.m.
To run LapRLS download k.m, LapRLS.m
and visit the manifold regularization page
to download adjacency.m and L2_distance.m.
To run Example 5.8 download ex58.m and WDMR.m.
To run Example 5.10 download ex510.m, robot.mat and WDMR.m.
**Chapter 6**

To run Example 6.2 download ex62.m, WDMR.m, fit.m, dataex62.mat and GRAY_WDMR.m. To run LapRLS download k.m, LapRLS.m
and visit the manifold regularization page
to download adjacency.m and L2_distance.m.