This is my third post on tensors and tensor decompositions. The first post on this topic primarily introduced tensors and some related terminology. The second post was meant to illustrate a particularly simple tensor decomposition method, called the CP decomposition. In this post, I will describe another tensor decomposition method, known as the Tucker decomposition. While the CP decomposition’s chief merit is its simplicity, it is limited in its approximation capability and it requires the same number of components in each mode. The Tucker decomposition, on the other hand, is extremely efficient in terms of approximation and allows different number of components in different modes. Before going any further, lets look at factor matrices and n-mode product of a tensor and a matrix.
Please read the complete post at
https://iksinc.tech/understanding-tensors-and-tensor-decompositions-part-3/(opens in a new tab)
From Data to Decisions 