Tensor Factorizations

The application of binary matrices are numerous. Representing a matrix as a mixture of a small collection of latent vectors via low-rank factorization is often seen as an advantageous method to interpret and analyze data. In this work, we examine the …

Recently, there is an emerging interest for applications of tensor factorization in big-data analytics and machine learning. Tensor factorization can extract latent features and perform dimension reduction that can facilitate discoveries of new …

This work aims to determine climate biomes directly from data, determine where they are changing, and assign a value of trust to our classification.

The goal of this work is to discover and analyze climatic signatures within E3SM MPAS-O data of the Southern Ocean.

In this work, we attempt to understand the mathematical connections between different types of tensor factorizations.