STATISTICS COLLOQUIUM
Anru Zhang, Assistant Professor
Department of Statistics
University of Wisconsin-Madison
Statistical Learning for High-dimensional Tensor Data
Abstract
The analysis of tensor data has become an active research topic in this area of big data. Datasets in the form of tensors, or high-order matrices, arise from a wide range of applications, such as financial econometrics, genomics, and material science. In addition, tensor methods provide unique perspectives and solutions to many high-dimensional problems, such as topic modeling and high-order interaction pursuit, where the observations are not necessarily tensors. High-dimensional tensor problems generally possess distinct characteristics that pose unprecedented challenges to the data science community. There is a clear need to develop new methods, efficient algorithms, and fundamental theory to analyze the high-dimensional tensor data.
In this talk, we discuss some recent advances in high-dimensional tensor data analysis through the consideration of several fundamental and interrelated problems, including tensor SVD and tensor regression. We illustrate how we develop new statistically optimal methods and computationally efficient algorithms that exploit useful information from high-dimensional tensor data based on the modern theories of computation, high-dimensional statistics, and non-convex optimization.
For more information, contact: Tracy Burke at tracy.burke@uconn.edu