Tensors in biological data and algebraic statistics
Tensors are higher dimensional analogues of matrices, used to record data with multiple changing variables. Interpreting tensor data requires finding multi-linear stucture that depends on the application or context. I will describe a tensor-based clustering method for multi-dimensional data. The multi-linear structure is encoded as algebraic constraints in a linear program. I apply the method to a collection of experiments measuring the response of genetically diverse breast cancer cell lines to an array of ligands. In the second part of the talk, I will discuss low-rank decompositions of tensors that arise in statistics, focusing on two graphical models with hidden variables. I describe how the implicit semi-algebraic description of the statistical models can be used to obtain a closed form expression for the maximum likelihood estimate.
Date:
21 February 2020, 14:00 (Friday, 5th week, Hilary 2020)
Venue:
Mathematical Institute, Woodstock Road OX2 6GG
Venue Details:
L2
Speaker:
Dr Anna Seigal (University of Oxford)
Organising department:
Mathematical Institute
Organiser:
Sara Jolliffe (University of Oxford)
Organiser contact email address:
sara.jolliffe@maths.ox.ac.uk
Host:
Philip Maini (University of Oxford)
Part of:
Mathematical Biology and Ecology
Booking required?:
Not required
Audience:
Public
Editor:
Sara Jolliffe
