Event Information:
Jason Morton
(Postdoctoral Fellow, Dept of Mathematics, Stanford University)
Algebraic Models for Multilinear Dependence
Statistical Science Seminar
Algebraic Models for Multilinear Dependence
- Abstract:
- We discuss a new statistical technique inspired by research in tensor
geometry and making use of cumulants, the higher order tensor analogs
of the covariance matrix. For non-Gaussian data not derived from
independent factors, tensor decomposition techniques for factor
analysis such as Principal Component Analysis and Independent
Component Analysis are inadequate. Seeking a small, closed space of
models which is computable and captures higher-order dependence leads
to a proposed extension of PCA and ICA, Principal Cumulant Component
Analysis (PCCA). Estimation is performed by maximization over a
Grassmannian. Joint work with L.-H. Lim.
http://math.stanford.edu/~jason/
Statistical Science Seminar