Introduction to Tensor Decompositions and their Applications in Machine Learning

Understand

Tensors are multidimensional arrays of numerical values and therefore generalize matrices to multiple dimensions.

• While tensors first emerged in the psychometrics community in the $20^{\text{th}}$ century, they have since then spread to numerous other disciplines, including machine learning.
• Tensors and their decompositions are especially beneficial in unsupervised learning settings, but are gaining popularity in other sub-disciplines like temporal and multi-relational data analysis, too.
• The scope of this paper is to give a broad overview of tensors, their decompositions, and how they are used in machine learning.