Eigendecompositions and Singular Value Decompositions appear in a variety of settings in machine learning and data mining. The eigendecomposition looks like so:

$\mathbf{Q}$ contains the eigenvectors of $\mathbf{A}$ and $\mathbf{\Lambda}$ is a diagonal matrix containing the eigenvalues.

The singular value decomposition looks like:

$\mathbf{U}$ contains the eigenvectors of the covariance matrix $\mathbf{A}\mathbf{A^T}$. $\mathbf{V}$ contains the eigenvectors of the gram matrix $\mathbf{A^T}\mathbf{A}$.

The truncated variants of these decompositions allow us to compute only a few eigenvalues(vectors) or singular values (vectors).

This is important since (i) a lot of times, the smaller eigenvalues are discarded, and (ii) you don’t want to compute the entire decomposition and retain only a few of the rows and columns of the computed matrices each time.

For core.matrix, I implemented these truncated decompositions in Kublai. Details below.