Mds is usually a way to visualize it close to truth. The rest looks like countours of a regular 2d kernel density estimate. On Aug 26, 2012 8:02 PM, "Lance Norskog" <[email protected]> wrote:
> This is a really cool 3D visualization of a tag cloud with distances: > http://langtech.jrc.ec.europa.eu/Pictures/ThemeScape-overview_EP259.pdf > > What is the sequence to make this? I'm thinking: > 1) Create a document/term matrix. > 2) Random Projection of term vectors onto 2D. > 2D distances match N-dimensional distances between terms. > 3) Do SVD of term vectors. > 4) Use first feature vector to select height of each term. > Or, norm of the feature vector X singular values. > > After this, the mapping software does the rest of the work via topo > and word placement algorithms. > > -- > Lance Norskog > [email protected] >
