Estimating the rate at which events happen has been studied under various guises and in different settings. We are interested in the specific case of consumerinitiated events or t...
We develop the notion of normalized information distance (NID) [7] into a kernel distance suitable for use with a Support Vector Machine classifier, and demonstrate its use for an...
We derive a number of well known deterministic latent variable models such as PCA, ICA, EPCA, NMF and PLSA as variational EM approximations with point posteriors. We show that the...
Max Welling, Chaitanya Chemudugunta, Nathan Sutter
We propose the use of random projections with a sparse matrix to maintain a sketch of a collection of high-dimensional data-streams that are updated asynchronously. This sketch al...
Aditya Krishna Menon, Gia Vinh Anh Pham, Sanjay Ch...
We propose an efficient algorithm for principal component analysis (PCA) that is applicable when only the inner product with a given vector is needed. We show that Krylov subspace...