There are definitely elements of spectral graph theory in my research too. I'll summarise
We are interested in seeing the each eigenvector from svd can represent in a semantic space In addition to this we'll be testing it against some algorithms like concept indexing (uses a bipartitional k-meansish method for dim reduction) also testing against Orthogonal Locality Preserving indexing, which uses the laplacian of a similarity matrix to calculate projections of a document (or term) into a manifold. These methods have been implemented and tested for document classification, I'm interested in seeing their applicability to modelling semantics with a system known as Hyperspace to analog language. I was hoping to do svd to my HAL built out of reuters, but that was way too big. instead i'm trying with the traces idea i mentioned before (ie contextually grepping a keyword out of the docs to build a space around it.) Cheers Dave On 5/14/07, Charles R Harris <[EMAIL PROTECTED]> wrote: > > > On 5/13/07, Dave P. Novakovic <[EMAIL PROTECTED]> wrote: > > > Are you trying some sort of principal components analysis? > > > > PCA is indeed one part of the research I'm doing. > > I had the impression you were trying to build a linear space in which to > embed a model, like atmospheric folk do when they try to invert spectra to > obtain thermal profiles. Model based compression would be another aspect of > this. I wonder if there aren't some algorithms out there for this sort of > thing. > > Chuck > > > _______________________________________________ > Numpy-discussion mailing list > [email protected] > http://projects.scipy.org/mailman/listinfo/numpy-discussion > > _______________________________________________ Numpy-discussion mailing list [email protected] http://projects.scipy.org/mailman/listinfo/numpy-discussion
