Search Research Symposium Abstracts

Hierarchical nonnegative tensor factorizations and applications

Nonnegative matrix factorization (NMF) has found many applications including topic modeling and document analysis. Hierarchical NMF (HNMF) variants are able to learn topics at various levels of granularity and illustrate their hierarchical relationship. Recently, nonnegative tensor factorization (NTF) methods have been applied in a similar fashion in order [Read More...]

Presenter: Jamie Haddock, Harvey Mudd College
Authors: Deanna Needell, Joshua Vendrow
Symposium Year: 2023
Session: Tensor Methods for data modeling [Organized by Anna Konstorum]
Presentation Time: September 30, 2023; 9:45 am

Recovering Noisy Tensors from Double Sketches

Machine learning tasks often utilize vast amounts of data where data can be represented in various ways. Instead of representing data as one or two-dimensional arrays, one can instead use multi-dimensional arrays to describe more complex relationships between elements in a data set. Moving beyond the matrix or vector representation of data requires using [Read More...]

Presenter: Anna Ma,
Authors: Yizhe Zhu, Dominik Stoeger
Symposium Year: 2023
Session: Tensor Methods for data modeling [Organized by Anna Konstorum]
Presentation Time: September 30, 2023; 10:10 am

Robust Tensor CUR: Rapid Low-Tucker-Rank Tensor Recovery with Sparse Corruptions

We study the problem of tensor robust principal component analysis (TRPCA), which aims to separate an underlying low-multilinear-rank tensor and a sparse outlier tensor from their sum. This work proposes fast algorithms, called Robust Tensor CUR(RTCUR), for large-scale non-convex TRPCA problems under the Tucker rank setting. RTCUR is developed within a [Read More...]

Presenter: Longxiu Huang, Michigan State University
Authors: HanQin Cai, Zehan Chao, Longxiu Huang, and Deanna Needell
Symposium Year: 2023
Session: Tensor Methods for data modeling [Organized by Anna Konstorum]
Presentation Time: September 30, 2023; 10:35 am

Tensor BM-Decomposition for Compression and Analysis of Spatio-Temporal Third-order Data

We introduce a third-order tensor decomposition framework based on a ternary multiplication named Bhattacharya-Mesner (BM) product and its corresponding notion of rank. We describe an iterative algorithm for computing a low BM-rank approximation to a given third-order data array. Moreover, we will demonstrate our decomposition framework for video processing [Read More...]

Presenter: Fan Tian, Tufts University
Authors: Fan Tian, Misha E. Kilmer, Eric Miller, Abani Patra
Symposium Year: 2023
Session: Tensor Methods for data modeling [Organized by Anna Konstorum]
Presentation Time: September 30, 2023; 11:00 am