On March 5, 2009 Rebecca Willett will be leading a discussion on High-Dimensional Co-Occurrence Density Estimation.

Location: 4219 French Family Science Center

Time: 1:00 – 2:00 pm

Abstract: Co-occurrence data can represent critical information in a variety of contexts, such as meetings in a social network, routers in a communication network, or genes, proteins, and metabolites in biological research. I will present a novel and efficient recursive algorithm for computing an orthogonal series density estimate in the Walsh basis, which allows for a flexible trade-off between estimation error and computational complexity. In particular, even when there are 2^d coefficients to estimate and d is very large, we can achieve near-minimax error decay rates with a computational complexity which is polynomial in d and depends on the density’s sparsity in the Walsh basis.
This is joint work with Maxim Raginsky, Svetlana Lazebnik, and Jorge Silva.