LossMat

The data matrix, with the N slices strung out as vectors in the columns.

The matrix of cluster means, with each mean represented by a row.

This function calculates the loss function for the interaction clustering
for all data slices and clusters means. The inputs are numeric matrices.

Functions for performing least-squares bilinear clustering of
three-way data. The method uses the bilinear decomposition (or bi-additive
model) to model two-way matrix slices while clustering over the third way.
Up to four different types of clusters are included, one for each term of the
bilinear decomposition. In this way, matrices are clustered simultaneously on
(a subset of) their overall means, row margins, column margins and row-column
interactions. The orthogonality of the bilinear model results in separability of
the joint clustering problem into four separate ones. Three of these sub-problems
are specific k-means problems, while a special algorithm is implemented for the
interactions. Plotting methods are provided, including biplots for the low-rank
approximations of the interactions.

Pieter Schoonees

LossMat: C++ Function for Interaction Loss Function

Description

Arguments

Value